Unified Deep Supervised Domain Adaptation and Generalization
Saeid Motiian

Marco Piccirilli Donald A. Adjeroh
West Virginia University
Morgantown, WV 26508

Gianfranco Doretto

arXiv:1709.10190v1 [] 28 Sep 2017

{samotiian, mpiccir1, daadjeroh, gidoretto}@mix.wvu.edu

data, SDA typically outperforms UDA, as we will later explain. Therefore, especially when target data is scarce, it is
more attractive to use SDA, also because limited amounts
of target data are likely to not be very expensive to label.
In the absence of target data, domain generalization
(DG) exploits several cheaply available datasets (sources),
representing different specific but closely related tasks. It
then attempts to learn by combining data sources in a way
that produces visual classifiers that are less sensitive to the
specific target data that will need to be processed.
In this work, we introduce a supervised approach for visual recognition that can be used for both SDA and DG.
The SDA approach requires very few labeled target samples per category in training. Indeed, even one sample can
significantly increase performance, and a few others bring
it closer to a peak, showing a remarkable “speed” of adaptation. Moreover, the approach is also robust to adapting to categories that have no target labeled samples. Although domain adaptation and generalization are closely
related, adaptation techniques are not directly applied to
DG, and viceversa. However, we show that by making simple changes to our proposed training loss function, and by
maintaining the same architecture, our SDA approach very
effectively extends to DG.
Using basic principles, we analyze how visual classification is extended to handle UDA by aligning a source domain distribution to a target domain distribution to make
the classifier domain invariant. This leads to observing that
SDA approaches improve upon UDA by making the alignment semantic, because they can ensure the alignment of semantically equivalent distributions from different domains.
However, we go one step ahead by suggesting that semantic
distribution separation should further increase performance,
and this leads to the introduction of a classification and contrastive semantic alignment (CCSA) loss.
We deal with the limited size of target domain samples
by observing that the CCSA loss relies on computing distances and similarities between distributions (as typically
done in adaptation and generalization approaches). Those
are difficult to represent with limited data. Thus, we revert

Abstract
This work provides a unified framework for addressing
the problem of visual supervised domain adaptation and
generalization with deep models. The main idea is to exploit
the Siamese architecture to learn an embedding subspace
that is discriminative, and where mapped visual domains
are semantically aligned and yet maximally separated. The
supervised setting becomes attractive especially when only
few target data samples need to be labeled. In this scenario,
alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by
reverting to point-wise surrogates of distribution distances
and similarities provides an effective solution. In addition,
the approach has a high “speed” of adaptation, which requires an extremely low number of labeled target training
samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.

1. Introduction
Many computer vision applications require enough labeled data (target data) for training visual classifiers to address a specific task at hand. Whenever target data is either
not available, or it is expensive to collect and/or label it, the
typical approach is to use available datasets (source data),
representative of a closely related task. Since this practice
is known for leading to suboptimal performance, techniques
such as domain adaptation [6] and/or domain generalization [5] have been developed to address the issue. Domain
adaptation methods require target data, whereas domain
generalization methods do not. Domain adaptation can be
either supervised [60, 33], unsupervised [38, 38, 61], or
semi-supervised [26, 29, 67]. Unsupervised domain adaptation (UDA) is attractive because it does not require target
data to be labeled. Conversely, supervised domain adaptation (SDA) requires labeled target data.
UDA expects large amounts of target data in order to
be effective, and this is emphasized even more when using
deep models. Moreover, given the same amount of target
1

Source

g

h
Classification
Loss

Target

Contrastive Semantic
Alignment Loss
Classification Loss

Separation Loss

Semantic Alignment Loss

Figure 1. Deep supervised domain adaptation. In training, the semantic alignment loss minimizes the distance between samples from
different domains but the same class label and the separation loss maximizes the distance between samples from different domains and
class labels. At the same time, the classification loss guarantees high classification accuracy.

to point-wise surrogates. The resulting approach turns out
to be very effective as shown in the experimental section.

2. Related work
Domain adaptation. Visual recognition algorithms are
trained with data from a source domain, and when they are
tested on a target domain with marginal distribution that differs from the one of the sources, we experience the visual
domain adaptation (DA) problem (also known as dataset
bias [48, 59, 58], or covariate shift [54]), and observe a performance decrease.
Traditional DA methods attempt to directly minimize
the shift between source and target distributions. We divide them in three categories. The first one includes those
that try to find a mapping between source and target distributions [53, 34, 27, 26, 19, 57]. The second one seeks
to find a shared latent space for source and target distributions [40, 2, 44, 21, 22, 47, 43]. The third one regularizes
a classifier trained on a source distribution to work well on
a target distribution [4, 1, 66, 15, 3, 12]. UDA approaches
fall in the first and second categories, while SDA methods
could fall either in the second or third category or sometimes both. Recently, [7, 42] have addressed UDA when
an auxiliary data view [36, 43], is available during training,
which is beyond the scope of this work.
Here, we are interested in finding a shared subspace for
source and target distributions. Among algorithms for subspace learning, Siamese networks [11] work well for different tasks [14, 55, 35, 63, 9]. Recently, Siamese networks
have been used for domain adaptation. In [60], which is
an SDA approach, unlabeled and sparsely labeled target domain data are used to optimize for domain invariance to facilitate domain transfer while using a soft label distribution
matching loss. In [56], which is a UDA approach, unlabeled
target data is used to learn a nonlinear transformation that
aligns correlations of layer activations in deep neural networks. Some approaches went beyond the Siamase weight-

sharing and used couple networks for DA. [33] uses two
CNN streams, for source and target, fused at the classifier
level. [50] uses a two-streams architecture, for source and
target, with related but not shared weights. Here we use
a Siamese network to learn an embedding such that samples from the same class are mapped as close as possible to
each other. This semantic alignment objective is similar to
other deep approaches, but unlike them, we explicitly model
and introduce cross-domain class separation forces. Moreover, we do so with very few training samples, which makes
the problem of characterizing distributions challenging, and
this is why we propose to use point-wise surrogates.
Domain generalization. Domain generalization (DG) is a
less investigated problem and is addressed in two ways. In
the first one, all information from the training domains or
datasets is aggregated to learn a shared invariant representation. Specifically, [5] pulls all of the training data together
in one dataset, and learns a single SVM classifier. [44]
learns an invariant transformation by minimizing the dissimilarity across domains. [23], which can be used for SDA
too, finds a representation that minimizes the mismatch between domains and maximizes the separability of data. [24]
learns features that are robust to variations across domains.
The second approach to DG is to exploit all information
from the training domains to train a classifier or regulate its
weights [32, 17, 65, 45, 46]. Specifically, [32] adjusts the
weights of the classifier to work well on an unseen dataset,
and [65] fuses the score of exemplar classifiers given any
test sample. While most works use the shallow models, here
we approach DG as in the first way, and extend the proposed
SDA approach by training a deep Siamese network to find
a shared invariant representation where semantic alignment
as well as separation are explicitly accounted for. To the
best of our knowledge, [24] is the only DG approach using
deep models, and our method is the first deep method that
solves both adaptation and generalization.

Testing

Training

g

g

Semantic Alignment Loss

Separation Loss

Classification Loss

Figure 2. Deep domain generalization. In training, the semantic alignment loss minimizes the distance between samples from different
domains but the same class label and the separation loss maximizes the distance between samples from different domains and class labels.
At the same time, the classification loss guarantees high classification accuracy. In testing, the embedding function embeds samples from
unseen distributions to the domain invariant space and the prediction function classifies them (right). In this figure, different colors represent
different domain distributions and different shapes represent different classes.

3. Supervised DA with Scarce Target Data
In this section we describe the model we propose to address supervised domain adaptation (SDA), and in the following Section 4 we extend it to address the domain generalization problem. We are given a training dataset made
s
of pairs Ds = {(xsi , yis )}N
i=1 . The feature xi ∈ X is a res
alization from a random variable X , and the label yis ∈ Y
is a realization from a random variable Y . In addition, we
are also given the training data Dt = {(xti , yit )}M
i=1 , where
xti ∈ X is a realization from a random variable X t , and the
labels yit ∈ Y. We assume that there is a covariate shift [54]
between X s and X t , i.e., there is a difference between the
probability distributions p(X s ) and p(X t ). We say that X s
represents the source domain and that X t represents the target domain. Under this settings the goal is to learn a prediction function f : X → Y that during testing is going to
perform well on data from the target domain.
The problem formulated thus far is typically referred to
as supervised domain adaptation. In this work we are especially concerned with the version of this problem where
only very few target labeled samples per class are available.
We aim at handling cases where there is only one target labeled sample, and there can even be some classes with no
target samples at all.

3.1. Deep SDA
In the absence of covariate shift a visual classifier f is
trained by minimizing a classification loss
LC (f ) = E[`(f (X s ), Y )] ,

(1)

where E[·] denotes statistical expectation and ` could be any
appropriate loss function (for example categorical crossentropy for multi-class classification). When the distributions of X s and X t are different, a deep model fs trained
with Ds will have reduced performance on the target domain. Increasing it would be trivial by simply training a
new model ft with data Dt . However, Dt is small and deep
models require large amounts of labeled data.

In general, f could be modeled by the composition of
two functions, i.e., f = h ◦ g. Here g : X → Z would
be an embedding from the input space X to a feature or
embedding space Z, and h : Z → Y would be a function
for predicting from the feature space. With this notation we
would have fs = hs ◦ gs and ft = ht ◦ gt , and the SDA
problem would be about finding the best approximation for
gt and ht , given the constraints on the available data.
The unsupervised DA paradigm (UDA) assumes that Dt
does not have labels. In that case the typical approach assumes that gt = gs = g, and f minimizes (1), while g also
minimizes
LCA (g) = d(p(g(X s )), p(g(X t ))) .

(2)

The purpose of (2) is to align the distributions of the features in the embedding space, mapped from the source and
the target domains. d is meant to be a metric between distributions that once aligned, they will no longer allow to tell
whether a feature is coming from the source or the target
domain. For that reason, we refer to (2) as the confusion
alignment loss. A popular choice for d is the Maximum
Mean Discrepancy [28]. In the embedding space Z, features are assumed to be domain invariant. Therefore, UDA
methods say that from the feature to the label space it is safe
to assume that ht = hs = h.
Since we are interested in visual recognition, the embedding function g would be modeled by a convolutional neural
network (CNN) with some initial convolutional layers, followed by some fully connected layers. In addition, the training architecture would have two streams, one for source and
the other for target samples. Since gs = gt = g, the CNN
parameters would be shared as in a Siamese architecture. In
addition, the source stream would continue with additional
fully connected layers for modeling h. See Figure 1.
From the above discussion it is clear that in order to per1 hapform
1 well, UDA needs to align effectively. This can
pen only if distributions are represented by a sufficiently
large dataset. Therefore, UDA approaches are in a position of weakness because we assume Dt to be small. More-

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Figure 3. Visualization of the MNIST-USPS datasets. Left: 2D visualization of the row images of the MNIST-USPS datasets. The
samples from the same class and different domains lie far from each other on the 2D subspace. Middle: 2D visualization of the embedded
images using our base model (without domain adaptation). The samples from the same class and different domains still lie far from each
other on the 2D subspace. Right: 2D visualization of the embedded images using our SDA model. The samples from the same class and
different domains lie very close to each other on the 2D subspace.

over, UDA approaches have also another intrinsic limitation, which is that even with perfect confusion alignment,
there is no guarantee that samples from different domains
but the same class label, would map nearby in the embedding space. This lack of semantic alignment is a major
source of performance reduction.
SDA approaches easily address the semantic alignment
problem by replacing (2) with
LSA (g) =

C
X

d(p(g(Xas )), p(g(Xat ))) ,

(3)

a=1

where C is the number of class labels, and Xas = X s |{Y =
a} and Xat = X t |{Y = a} are conditional random variables. d instead is a suitable distance metric between the
distributions of Xas and Xat in the embedding space. We
refer to (3) as the semantic alignment loss, which clearly
encourages samples from different domains but the same
label, to map nearby in the embedding space.
While the analysis above clearly indicates why SDA provides superior performance than UDA, it also suggests that
deep SDA approaches have not considered that greater performance could be achieved by encouraging class separation, meaning that samples from different domains and with
different labels, should be mapped as far apart as possible in
the embedding space. This idea means that, in principle, a
semantic alignment less prone to errors should be achieved
by adding to (3) the following term
X
LS (g) =
k(p(g(Xas )), p(g(Xbt ))) ,
(4)
a,b|a6=b

where k is a suitable similarity metric between the distributions of Xas and Xbt in the embedding space, which adds

a penalty when the distributions p(g(Xas )) and p(g(Xbt ))
come close, since they would lead to lower classification
accuracy. We refer to (4) as the separation loss.
Finally, we suggest that SDA could be approached by
learning a deep model f = h ◦ g such that
LCCSA (f ) = LC (h ◦ g) + LSA (g) + LS (g) .

(5)

We refer to (5) as the classification and contrastive semantic
alignment loss. This would allow to set gs = gt = g. The
classification network h is trained only with source data, so
hs = h. In addition, to improve performance on the target
domain, ht could be obtained via fine-tuning based on the
few samples in Dt , i.e.,
ht = fine-tuning(h|Dt ) .

(6)

Note that the network architecture remains the one in Figure 1, only with a different loss, and training procedure.

3.2. Handling Scarce Target Data
When the size of the labeled target training dataset Dt
is very small, minimizing the loss (5) becomes a challenge.
The problem is that the semantic alignment loss as well as
the separation loss rely on computing distances and similarities between distributions, and those are very difficult to
represent with as few as one data sample.
Rather than attempting to characterize distributions with
statistics that require enough data, because of the reduced
size of Dt , we compute the distance in the semantic alignment loss (3) by computing average pairwise distances between points in the embedding space, i.e., we compute
X
d(p(g(Xas )), p(g(Xat ))) =
d(g(xsi ), g(xtj )) ,
(7)
i,j

(a) Office-31

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Ours

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Accuracy

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75

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Ours

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85
80
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CS
CSA
CCSA

70
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Average

600

1

2

3

4

5

6

7

Number of samples per category (n)

8

Figure 4. (a), (b), (c): Improvement of CCSA over the base model.
(d): Average classification accuracy for M → U task for different
number of labeled target samples per category (n). It shows that
our model provides significant improvement over baselines.

where it is assumed yis = yjt = a. The strength of this approach is that it allows even a single labeled target sample
to be paired with all the source samples, effectively trying
to semantically align the entire source data with the few target data. Similarly, we compute the similarities in the separation loss (4) by computing average pairwise similarities
between points in the embedding space, i.e., we compute
X
k(p(g(Xas )), p(g(Xbt ))) =
k(g(xsi ), g(xtj )) ,
(8)

available during training, thus representative of an unknown
target domain.
The SDA method in Section 3 treats source and target
datasets Ds and Dt almost symmetrically. In particular, the
embedding g aims at achieving semantic alignment, while
favoring class separation. The only asymmetry is in the prediction function h that is trained only on the source, to be
then fine-tuned on the target.
In domain generalization, we are not interested in adapting the classifier to the target domain, because it is unknown. Instead, we want to make sure that the embedding
g maps to a domain invariant space. To do so we consider
every distinct unordered pair of source domains (u, v), represented by Dsu and Dsv , and, like in SDA, impose the semantic alignment loss (3) as well as the separation loss (4).
Moreover, the losses are summed over every pair in order to
make the map g as domain invariant as possible. Similarly,
the classifier h should be as correct as possible for any of
the mapped samples, to maximize performance on an unseen target. This calls for having a fully symmetric learning
for h by training it on all the source domains, meaning that
the classification loss (1) is summed over every domain su .
See Figure 2.
The network architecture is still the one in Figure 1, and
we have implemented it with the same choices for distances
and similarities as those made in Section 3.2. However,
since we are summing the losses (3) and (4) over every unordered pair of source domains, there is a quadratic growth
of paired training samples. So, if necessary, rather than processing every paired sample, we select them randomly.

i,j

where it is assumed that yis = a 6= yjt = b.
Moreover, our implementation further assumes that
d(g(xsi ), g(xtj ))

=

k(g(xsi ), g(xtj ))

=

1
kg(xsi ) − g(xtj )k2 ,
(9)
2
1
max(0, m − kg(xsi ) − g(xtj )k)2 (10)
2

where k · k denotes the Frobenius norm, and m is the margin that specifies the separability in the embedding space.
Note that with the choices outlined in (9) and (10), the loss
LSA (g)+LS (g) becomes the well known contrastive loss as
defined in [30]. Finally, to balance the classification versus
the contrastive semantic alignment portion of the loss (5),
(7) and (8) are normalized and weighted by 1 − γ and (1)
by γ.

4. Extension to Domain Generalization
In visual domain generalization (DG), D labeled datasets
Ds1 , · · · , DsD , representative of D distinct source domains
are given. The goal is to learn from them a visual classifier
f that during testing is going to perform well on data Dt , not

5. Experiments
We divide the experiments into two parts, domain adaptation and domain generalization. In both sections, we use
benchmark datasets and compare our domain adaptation
model and our domain generalization model, both indicated
as CCSA, with the state-of-the-art.

5.1. Domain Adaptation
We present results using the Office dataset [53], the
MNIST dataset [37], and the USPS dataset [31].
5.1.1

Office Dataset

The office dataset is a standard benchmark dataset for visual
domain adaptation. It contains 31 object classes for three
domains: Amazon, Webcam, and DSLR, indicated as A,
W, and D, for a total of 4,652 images. We consider six
domain shifts using the three domains (A → W, A → D,
W → A, W → D, D → A, and D → W). We performed
different experiments using this dataset.
First experiment. We followed the setting described
in [60]. All classes of the office dataset and 5 train-test splits

Table 1. Office dataset. Classification accuracy for domain adaptation over the 31 categories of the Office dataset. A, W, and D stand for
Amazon, Webcam, and DSLR domain. Lower Bound is our base model without adaptation.
A→W
A→D
W→A
W→D
D→A
D→W
Average

Lower Bound
61.2 ± 0.9
62.3 ± 0.8
51.6 ± 0.9
95.6 ± 0.7
58.5 ± 0.8
80.1 ± 0.6
68.2

Unsupervised
[62]
[39]
[25]
61.8 ± 0.4 68.5 ± 0.4 68.7 ± 0.3
64.4 ± 0.3 67.0 ± 0.4 67.1 ± 0.3
52.2 ± 0.4 53.1 ± 0.3 54.09 ± 0.5
98.5 ± 0.4 99.0 ± 0.2 99.0 ± 0.2
52.1 ± 0.8 54.0 ± 0.4 56.0 ± 0.5
95.0 ± 0.5 96.0 ± 0.3 96.4 ± 0.3
70.6
72.9
73.6

Table 2. Office dataset. Classification accuracy for domain adaptation over the Office dataset when only the labeled target samples
of 15 classes are available during training. Testing is done on all
31 classes. A, W, and D stand for Amazon, Webcam, and DSLR
domain. Lower Bound is our base model without adaptation.
A→W
A→D
W→A
W→D
D→A
D→W
Average

Lower Bound
52.1 ± 0.6
61.6 ± 0.8
34.5 ± 0.9
95.1 ± 0.2
40.1 ± 0.3
89.7 ± 0.8
62.26

[60]
59.3 ± 0.6
68.0 ± 0.5
40.5 ± 0.2
97.5 ± 0.1
43.1 ± 0.2
90.0 ± 0.2
66.4

CCSA
63.3 ± 0.9
70.5 ± 0.6
43.6 ± 1.0
96.2 ± 0.3
42.6 ± 0.6
90.0 ± 0.2
67.83

are considered. For the source domain, 20 examples per category for the Amazon domain, and 8 examples per category
for the DSLR and Webcam domains are randomly selected
for training for each split. Also, 3 labeled examples are randomly selected for each category in the target domain for
training for each split. The rest of the target samples are
used for testing. Note that we used the same splits generated by [60]. We also report the classification results of the
SDA algorithm presented in [39] and [33]. In addition to the
SDA algorithms, we report the results of some recent UDA
algorithms. They follow a different experimental protocol
compared to the SDA algorithms, and use all samples of the
target domain in training as unlabeled data together with all
samples of the source domain.
For the embedding function g, we used the convolutional
layers of the VGG-16 architecture [55] followed by 2 fully
connected layers with output size of 1024 and 128, respectively. For the prediction function h, we used a fully connected layer with softmax activation. Similar to [60], we
used the weights pre-trained on the ImageNet dataset [51]
for the convolutional layers, and initialized the fully connected layers using all the source domain data. We then
fine-tuned all the weights using the train-test splits.
Table 1 reports the classification accuracy over 31 classes
for the Office dataset and shows that CCSA has better performance compared to [60]. Since the difference between
W domain and D domain is not considerable, unsupervised
algorithms work well on D → W and W → D. However,
in the cases when target and source domains are very different (A → W, W → A, A → D, and D → A), CCSA
shows larger margins compared to the second best. This

[60]
82.7 ± 0.8
86.1 ± 1.2
65.0 ± 0.5
97.6 ± 0.2
66.2 ± 0.3
95.7 ± 0.5
82.21

Supervised
[33]
84.5 ± 1.7
86.3 ± 0.8
65.7 ± 1.7
97.5 ± 0.7
66.5 ± 1.0
95.5 ± 0.6
82.68

CCSA
88.2 ± 1.0
89.0 ± 1.2
72.1 ± 1.0
97.6 ± 0.4
71.8 ± 0.5
96.4 ± 0.8
85.8

suggests that CCSA will provide greater alignment gains
when there are bigger domain shifts. Figure 4(a) instead,
shows how much improvement can be obtained with respect
to the base model. This is simply obtained by training g and
h with only the classification loss and source training data,
so no adaptation is performed.
Second experiment. We followed the setting described
in [60] when only 10 target labeled samples of 15 classes
of the Office dataset are available during training. Similar
to [60], we compute the accuracy on the remaining 16 categories for which no target data was available during training. We used the same network structure as in the first experiment and the same splits generated by [60].
Table 2 shows that CCSA is effective at transferring information from the labeled classes to the unlabeled target
classes. Similar to the first experiment, CCSA works well
when shifts between domains are larger.
Third experiment. We used the original train-test splits of
the Office dataset [53]. The splits are generated in a similar manner to the first experiment but here instead, only
10 classes are considered (backpack, bike, calculator, headphones, keyboard, laptop-computer, monitor, mouse, mug,
and projector). In order to compare our results with the
state-of-the-art, we used DeCaF-fc6 features [14] and 800dimension SURF features as input. For DeCaF-fc6 features
(SURF features) we used 2 fully connected layers with output size of 1024 (512) and 128 (32) with ReLU activation as
the embedding function, and one fully connected layer with
softmax activation as the prediction function. The features
and splits are available on the Office dataset webpage 1 .
We compared our results with three UDA (GFK [26],
mSDA [8], and RTML [13]) and one SDA (CDML [64])
algorithms under the same settings. Table 3 shows that
CCSA provides an improved accuracy with respect to the
others. Again, greater domain shifts are better compensated
by CCSA . Figure 4(b) shows the improvement of CCSA
over the base model using DeCaF-fc6 features.
5.1.2

MNIST-USPS Datasets

The MNIST (M) and USPS (U) datasets have recently been
used for domain adaptation [20, 50]. They contain images
1 https://cs.stanford.edu/

˜jhoffman/domainadapt/

Table 3. Office dataset. Classification accuracy for domain adaptation over the 10 categories of the Office dataset. A, W, and D stand for
Amazon, Webcam, and DSLR domain. Lower Bound is our base model with no adaptation.
Lower Bound

GFK [26]

A→W
A→D
W→A
W→D
D→A
D→W
Average

26.5 ± 3.1
17.5 ± 1.2
25.9 ± 1.0
46.9 ± 1.1
19.3 ± 1.9
48.0 ± 2.1
30.6

39.9 ± 0.9
36.2 ± 0.7
29.8 ± 0.6
80.9 ± 0.4
33.2 ± 0.6
79.4 ± 0.6
43.5

A→W
A→D
W→A
W→D
D→A
D→W
Average

78.9 ± 1.8
79.2 ± 2.1
77.3 ± 1.1
96.6 ± 1.0
84.0 ± 1.3
96.7 ± 0.9
85.4

73.1 ± 2.8
82.6 ± 2.1
82.6 ± 1.3
98.8 ± 0.9
85.4 ± 0.7
91.3 ± 0.4
85.63

mSDA [8] CDML [64]
SURF
35.5 ± 0.5
37.3 ± 0.7
29.7 ± 0.7
35.3 ± 0.5
32.1 ± 0.8
32.4 ± 0.5
56.6 ± 0.4
77.9 ± 0.9
33.6 ± 0.8
29.4 ± 0.8
68.6 ± 0.7
79.4 ± 0.6
38.4
43.5
DeCaF-fc6
64.6 ± 4.2
75.9 ± 2.1
72.6 ± 3.5
81.4 ± 2.6
71.4 ± 1.7
86.3 ± 1.6
99.5 ± 0.6
99.4 ± 0.4
78.8 ± 0.5
88.4 ± 0.5
97.5 ± 0.4
95.1 ± 0.5
80.73
87.75

Table 4. MNIST-USPS datasets. Classification accuracy for domain adaptation over the MNIST and USPS datasets. M and
U stand for MNIST and USPS domain. Lower Bound is our
base model without adaptation. CCSA - n stands for our method
when we use n labeled target samples per category in training.
Method
ADDA [61]
CoGAN [38]
Lower Bound
CCSA-1
CCSA-2
CCSA-3
CCSA-4
CCSA-5
CCSA-6
CCSA-7
CCSA-8

M→U
89.4
91.2
65.4
85.0
89.0
90.1
91.4
92.4
93.0
92.9
92.8

U →M
90.1
89.1
58.6
78.4
82.0
85.8
86.1
88.8
89.6
89.4
90.0

Average
89.7
90.1
62.0
81.7
85.5
87.9
88.7
90.1
91.3
91.1
91.4

Table 5. VLCS dataset. Classification accuracy for domain generalization over the 5 categories of the VLCS dataset. LB (Lower
Bound) is our base model trained without the contrastive semantic
alignment loss. 1NN stands for first nearest neighbor.
L, C, S → V
V, C, S → L
V, L, S → C
V, L, C → S
C, S → V, L
C, L → V, S
V, C → L, S
Average

Lower Bound
1NN SVM LB
57.2 58.4 59.1
52.4 55.2 55.6
90.5 85.1 86.1
56.9 55.2 54.6
55.0 55.5 55.3
52.6 51.8 50.9
56.6 59.9 60.1
60.1 60.1 60.2

UML [17]
56.2
58.5
91.1
58.4
56.4
57.4
55.4
61.5

Domain Generalization
LRE-SVM [65] SCA [23]
60.5
64.3
59.7
59.6
88.1
88.9
54.8
59.2
55.0
59.5
52.8
55.9
58.8
60.7
61.4
64.0

CCSA
67.1
62.1
92.3
59.1
59.3
56.5
60.2
65.0

of digits from 0 to 9. We considered two cross-domain
tasks, M → U and U → M, and followed the experimental
setting in [20, 50], which involves randomly selecting 2000
images from MNIST and 1800 images from USPS. Here,
we randomly selected n labeled samples per class from target domain data and used them in training. We evaluated
our approach for n ranging from 1 to 8 and repeated each
experiment 10 times (we only show the mean of the accuracies because the standard deviation is very small).
Similar to [37], we used 2 convolutional layers with 6
and 16 filters of 5 × 5 kernels followed by max-pooling
layers and 2 fully connected layers with size 120 and 84

RTML [13]

CCSA

43.4 ± 0.9
43.3 ± 0.6
37.5 ± 0.7
91.7 ± 1.1
36.3 ± 0.3
90.5 ± 0.7
49.8

71.2 ± 1.3
74.2 ± 1.3
42.9 ± 0.9
85.1 ± 1.0
28.9 ± 1.3
77.3 ± 1.6
63.2

79.5 ± 2.6
83.8 ± 1.7
90.8 ± 1.6
100 ± 0.0
90.6 ± 0.5
98.6 ± 0.3
90.55

94.5 ± 1.9
97.2 ± 1.0
91.2 ± 0.8
99.6 ± 0.5
91.7 ± 1.0
98.7 ± 0.6
95.4

as the embedding function g, and one fully connected layer
with softmax activation as the prediction function h. We
compare our method with 2 recent UDA methods. Those
methods use all target samples in their training stage, while
we only use very few labeled target samples per category in
training.
Table 4 shows the average classification accuracy of the
MNIST-USPS datasets. CCSA works well even when only
one target sample per category (n = 1) is available in training. Also, we can see that by increasing n, the accuracy
quickly converges to the top.
Ablation study. We consider three baselines to compare
with CCSA for M → U task. First, we train the network
with source data and then fine-tune it with available target
data. Second, we train the network using the classification
and semantic alignment losses (LCSA (f ) = LC (h ◦ g) +
LSA (g)). Third, we train the network using the classification and separation losses (LCS (f ) = LC (h ◦ g) + LS (g)).
Figure 4(d) shows the average accuracies over 10 repetitions. It shows that CSA and CS improve the accuracy over
fine-tuning. Using the semantic alignment loss together
with separation loss (CCSA) shows the best performance.
Visualization. We show how samples lie on the embedding
space using CCSA . First, we considered the row images
of the MNIST and USPS datasets and plotted 2D visualization of them using t-SNE algorithm [41]. As Figure 3(Left)
shows the row images of the same class and different domains lie far away from each other in the 2D subspace. For
example, the samples of the class zero of the USPS dataset
(0 U ) are far from the class zero of the MNIST dataset
(0 M ). Second, we trained our base model with no adaptation on the MNIST dataset. We then plotted the 2D visualization of the MNIST and USPS samples in the embedding space (output of g, the last fully connected layer). As
Figure 3(Middle) shows, the samples from the same class
and different domains still lie far away from each other in
the 2D subspace. Finally, we trained our SDA model on the

MNIST dataset and 3 labeled samples per class of the USPS
dataset. We then plotted the 2D visualization of the MNIST
and USPS samples in the embedding space (output of g).
As Figure 3(Right) shows, the samples from the same class
and different domains now lie very close to each other in the
2D subspace. Note however, that this is only a 2D visualization of high-dimensional data, and Figure 3(Right) may not
perfectly reflect how close is the data from the same class,
and how classes are separated.
Weight sharing: There is no restriction whether gt and gs
should share the weights or not. Not sharing weights will
likely lead to overfitting, given the reduced amount of target
training data, and weight-sharing can be seen as a regularizer. We repeated the experiment for M → U task when
n = 4. Not sharing weights provides the average accuracy
88.6 over 10 repetitions which is less than the average accuracy with weight-sharing (see Table 4). A similar behavior
can be seen for other experiments.

Table 6. MNIST dataset. Classification accuracy for domain generalization over the MNIST dataset and its rotated domains.
M15◦ , M30◦ , M45◦ , M60◦ , M75◦ → M
M, M30◦ , M45◦ , M60◦ , M75◦ → M15◦
M, M15◦ , M45◦ , M60◦ , M75◦ → M30◦
M, M15◦ , M30◦ , M60◦ , M75◦ → M45◦
M, M15◦ , M30◦ , M45◦ , M75◦ → M60◦
M, M15◦ , M30◦ , M45◦ , M60◦ → M75◦
Average

CAE [49]
72.1
95.3
92.6
81.5
92.7
79.3
85.5

MTAE [24]
82.5
96.3
93.4
78.6
94.2
80.5
87.5

CCSA
84.6
95.6
94.6
82.9
94.8
82.1
89.1

We report comparative results in Table 5, where all DG
methods work better than the base model, emphasizing the
need for domain generalization. Our DG method has higher
average performance. Also, note that in order to compare
with the state-of-the-art DG methods, we only used 2 fully
connected layers for our network and precomputed features
as input. However, when using convolutional layers on row
images, we expect our DG model to provide better performance. Figure 4(c) shows the improvement of our DG
model over the base model using DeCaF-fc6 features.

5.2. Domain Generalization
We evaluate CCSA on different datasets. The goal is to
show that CCSA is able to learn a domain invariant embedding subspace for visual recognition tasks.

5.3. VLCS Dataset
In this section, we use images of 5 shared object categories (bird, car, chair, dog, and person), of the PASCAL VOC2007 (V) [16], LabelMe (L) [52], Caltech-101
(C) [18], and SUN09 (S) [10] datasets, which is known as
VLCS dataset [17].
[24, 23] have shown that there are covariate shifts between the above 4 domains and have developed a DG
method to minimize them. We followed their experimental setting, and randomly divided each domain into a
training set (70%) and a test set (30%) and conducted a
leave-one-domain-out evaluation (4 cross-domain
cases) and a leave-two-domain-out evaluation (3
cross-domain cases). In order to compare our results with
the state-of-the-art, we used DeCaF-fc6 features which are
publicly available 2 , and repeated each cross-domain case
20 times and reported the average classification accuracy.
We used 2 fully connected layers with output size of
1024 and 128 with ReLU activation as the embedding function g, and one fully connected layer with softmax activation as the prediction function h. To create positive and
negative pairs for training our network, for each sample of a
source domain we randomly selected 5 samples from each
remaining source domain, and help in this way to avoid
overfitting. However, to train a deeper network together
with convolutional layers, it is enough to create a large
amount of positive and negative pairs.
2 http://www.cs.dartmouth.edu/

page/FXR_iccv13/index.php

˜chenfang/proj_

5.4. MNIST Dataset
We followed the setting in [24], and randomly selected a
set M of 100 images per category from the MNIST dataset
(1000 in total). We then rotated each image in M five
times with 15 degrees intervals, creating five new domains
M15◦ , M30◦ , M45◦ , M60◦ , and M75◦ . We conducted a
leave-one-domain-out evaluation (6 cross-domain
cases in total). We used the same network of Section 5.1.2,
and we repeated the experiments 10 times. To create positive and negative pairs for training our network, for each
sample of a source domain we randomly selected 2 samples
from each remaining source domain. We report comparative average accuracies for CCSA and others in Table 6,
showing again a performance improvement.

6. Conclusions
We have introduced a deep model in combination
with the classification and contrastive semantic alignment
(CCSA) loss to address SDA. We have shown that the
CCSA loss can be augmented to address the DG problem
without the need to change the basic model architecture.
However, the approach is general in the sense that the architecture sub-components can be changed. We found that
addressing the semantic distribution alignments with pointwise surrogates of distribution distances and similarities for
SDA and DG works very effectively, even when labeled target samples are very few. In addition, we found the SDA
accuracy to converge very quickly as more labeled target
samples per category are available.

Acknowledgments
This material is based upon work supported by the Center
for Identification Technology Research and the National Science
Foundation under Grant No. 1066197.

References
[1] Y. Aytar and A. Zisserman. Tabula rasa: Model transfer for
object category detection. In Computer Vision (ICCV), 2011
IEEE International Conference on, pages 2252–2259. IEEE,
2011.
[2] M. Baktashmotlagh, M. T. Harandi, B. C. Lovell, and
M. Salzmann. Unsupervised domain adaptation by domain
invariant projection. In IEEE ICCV, pages 769–776, 2013.
[3] C. J. Becker, C. M. Christoudias, and P. Fua. Non-linear
domain adaptation with boosting. In Advances in Neural
Information Processing Systems, pages 485–493, 2013.
[4] A. Bergamo and L. Torresani. Exploiting weakly-labeled
web images to improve object classification: a domain adaptation approach. In Advances in Neural Information Processing Systems, pages 181–189, 2010.
[5] G. Blanchard, G. Lee, and C. Scott. Generalizing from several related classification tasks to a new unlabeled sample. In
Advances in neural information processing systems, pages
2178–2186, 2011.
[6] J. Blitzer, R. McDonald, and F. Pereira. Domain adaptation with structural correspondence learning. In Proceedings
of the 2006 conference on empirical methods in natural language processing, pages 120–128. Association for Computational Linguistics, 2006.
[7] L. Chen, W. Li, and D. Xu. Recognizing RGB images by
learning from RGB-D data. In CVPR, pages 1418–1425,
June 2014.
[8] M. Chen, Z. Xu, K. Weinberger, and F. Sha. Marginalized denoising autoencoders for domain adaptation. arXiv preprint
arXiv:1206.4683, 2012.
[9] Q. Chen, J. Huang, R. Feris, L. M. Brown, J. Dong, and
S. Yan. Deep domain adaptation for describing people based
on fine-grained clothing attributes. In Proceedings of the
IEEE conference on computer vision and pattern recognition, pages 5315–5324, 2015.
[10] M. J. Choi, J. J. Lim, A. Torralba, and A. S. Willsky. Exploiting hierarchical context on a large database of object categories. In Computer vision and pattern recognition (CVPR),
2010 IEEE conference on, pages 129–136. IEEE, 2010.
[11] S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity metric discriminatively, with application to face verification. In Computer Vision and Pattern Recognition, 2005.
CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 539–546. IEEE, 2005.
[12] H. Daume III and D. Marcu. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research,
26:101–126, 2006.
[13] Z. Ding and Y. Fu. Robust transfer metric learning for image classification. IEEE Transactions on Image Processing,
26(2):660–670, 2017.
[14] J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang,
E. Tzeng, and T. Darrell.
DeCAF: a deep convolutional activation feature for generic visual recognition. In
arXiv:1310.1531, 2013.
[15] L. Duan, I. W. Tsang, D. Xu, and S. J. Maybank. Domain
transfer svm for video concept detection. In Computer Vi-

[16]

[17]

[18]

[19]

[20]

[21]
[22]

[23]

[24]

[25]

[26]

[27]

[28]

[29]

[30]

sion and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1375–1381. IEEE, 2009.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and
A. Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303–
338, 2010.
C. Fang, Y. Xu, and D. N. Rockmore. Unbiased metric learning: On the utilization of multiple datasets and web images
for softening bias. In International Conference on Computer
Vision, 2013.
L. Fei-Fei, R. Fergus, and P. Perona. Learning generative
visual models from few training examples: An incremental
bayesian approach tested on 101 object categories. Computer
vision and Image understanding, 106(1):59–70, 2007.
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars. Unsupervised visual domain adaptation using subspace alignment. In IEEE ICCV, pages 2960–2967, 2013.
B. Fernando, T. Tommasi, and T. Tuytelaarsc. Joint crossdomain classification and subspace learning for unsupervised adaptation. Pattern Recogition Letters, 2015.
Y. Ganin and V. Lempitsky. Unsupervised domain adaptation
by backpropagation. arXiv preprint arXiv:1409.7495, 2014.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle,
F. Laviolette, M. Marchand, and V. Lempitsky. Domainadversarial training of neural networks. Journal of Machine
Learning Research, 17(59):1–35, 2016.
M. Ghifary, D. Balduzzi, W. B. Kleijn, and M. Zhang. Scatter
component analysis: A unified framework for domain adaptation and domain generalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
M. Ghifary, W. Bastiaan Kleijn, M. Zhang, and D. Balduzzi.
Domain generalization for object recognition with multi-task
autoencoders. In Proceedings of the IEEE International Conference on Computer Vision, pages 2551–2559, 2015.
M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and
W. Li. Deep reconstruction-classification networks for unsupervised domain adaptation. In European Conference on
Computer Vision, pages 597–613. Springer, 2016.
B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In Computer Vision
and Pattern Recognition (CVPR), 2012 IEEE Conference on,
pages 2066–2073. IEEE, 2012.
R. Gopalan, R. Li, and R. Chellappa. Domain adaptation
for object recognition: An unsupervised approach. In IEEE
ICCV, pages 999–1006, 2011.
A. Gretton, K. M. Borgwardt, M. Rasch, B. Schölkopf, and
A. J. Smola. A kernel method for the two-sample-problem.
In NIPS, 2006.
Y. Guo and M. Xiao. Cross language text classification via
subspace co-regularized multi-view learning. In Proceedings
of the 29th International Conference on Machine Learning,
ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1,
2012, 2012.
R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In Computer vision
and pattern recognition, 2006 IEEE computer society conference on, volume 2, pages 1735–1742. IEEE, 2006.

[31] J. J. Hull. A database for handwritten text recognition research. IEEE Transactions on pattern analysis and machine
intelligence, 16(5):550–554, 1994.
[32] A. Khosla, T. Zhou, T. Malisiewicz, A. A. Efros, and A. Torralba. Undoing the damage of dataset bias. In European
Conference on Computer Vision, pages 158–171. Springer,
2012.
[33] P. Koniusz, Y. Tas, and F. Porikli. Domain adaptation by mixture of alignments of second- or higher-order scatter tensors.
In The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), July 2017.
[34] B. Kulis, K. Saenko, and T. Darrell. What you saw is not
what you get: Domain adaptation using asymmetric kernel transforms. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1785–1792.
IEEE, 2011.
[35] B. Kumar, G. Carneiro, I. Reid, et al. Learning local image descriptors with deep siamese and triplet convolutional
networks by minimising global loss functions. In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition, pages 5385–5394, 2016.
[36] M. Lapin, M. Hein, and B. Schiele. Learning using privileged information: SVM+ and weighted SVM. Neural Networks, 53:95–108, 2014.
[37] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradientbased learning applied to document recognition. Proceedings
of the IEEE, 86(11):2278–2324, 1998.
[38] M.-Y. Liu and O. Tuzel. Coupled generative adversarial networks. In Advances in Neural Information Processing Systems, pages 469–477, 2016.
[39] M. Long, Y. Cao, J. Wang, and M. I. Jordan. Learning transferable features with deep adaptation networks. In ICML,
pages 97–105, 2015.
[40] M. Long, G. Ding, J. Wang, J. Sun, Y. Guo, and P. S. Yu.
Transfer sparse coding for robust image representation. In
Proceedings of the IEEE conference on computer vision and
pattern recognition, pages 407–414, 2013.
[41] L. v. d. Maaten and G. Hinton. Visualizing data using t-sne.
Journal of Machine Learning Research, 9(Nov):2579–2605,
2008.
[42] S. Motiian and G. Doretto. Information bottleneck domain
adaptation with privileged information for visual recognition. In European Conference on Computer Vision, pages
630–647. Springer, 2016.
[43] S. Motiian, M. Piccirilli, D. A. Adjeroh, and G. Doretto. Information bottleneck learning using privileged information
for visual recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages
1496–1505, 2016.
[44] K. Muandet, D. Balduzzi, and B. Schölkopf. Domain generalization via invariant feature representation. In ICML (1),
pages 10–18, 2013.
[45] L. Niu, W. Li, and D. Xu. Multi-view domain generalization
for visual recognition. In Proceedings of the IEEE International Conference on Computer Vision, pages 4193–4201,
2015.

[46] L. Niu, W. Li, D. Xu, and J. Cai. An exemplar-based multiview domain generalization framework for visual recognition. IEEE Transactions on Neural Networks and Learning
Systems, 2017.
[47] S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang. Domain
adaptation via transfer component analysis. IEEE TNN,
22(2):199–210, 2011.
[48] J. Ponce, T. L. Berg, M. Everingham, D. A. Forsyth,
M. Hebert, S. Lazebnik, M. Marszalek, C. Schmid, B. C.
Russell, A. Torralba, et al. Dataset issues in object recognition. In Toward category-level object recognition, pages
29–48. Springer, 2006.
[49] S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio.
Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th international
conference on machine learning (ICML-11), pages 833–840,
2011.
[50] A. Rozantsev, M. Salzmann, and P. Fua. Beyond sharing weights for deep domain adaptation. arXiv preprint
arXiv:1603.06432, 2016.
[51] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh,
S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein,
A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual
Recognition Challenge. IJCV, 2015.
[52] B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. Labelme: a database and web-based tool for image
annotation. International journal of computer vision, 77(13):157–173, 2008.
[53] K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV, pages 213–
226, 2010.
[54] H. Shimodaira. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of
Statistical Planning and Inference, 90(2):227–244, 2000.
[55] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR,
abs/1409.1556, 2014.
[56] B. Sun and K. Saenko. Deep coral: Correlation alignment for
deep domain adaptation. In Computer Vision–ECCV 2016
Workshops, pages 443–450. Springer, 2016.
[57] T. Tommasi, M. Lanzi, P. Russo, and B. Caputo. Learning
the roots of visual domain shift. In Computer Vision–ECCV
2016 Workshops, pages 475–482. Springer, 2016.
[58] T. Tommasi, N. Patricia, B. Caputo, and T. Tuytelaars. A
deeper look at dataset bias. In German Conference on Pattern
Recognition, pages 504–516. Springer, 2015.
[59] A. Torralba and A. A. Efros. Unbiased look at dataset bias.
In Computer Vision and Pattern Recognition (CVPR), 2011
IEEE Conference on, pages 1521–1528, 2011.
[60] E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko. Simultaneous deep transfer across domains and tasks. In ICCV, 2015.
[61] E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell. Adversarial
discriminative domain adaptation. In The IEEE Conference
on Computer Vision and Pattern Recognition (CVPR), July
2017.
[62] E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell.
Deep domain confusion: Maximizing for domain invariance.
arXiv preprint arXiv:1412.3474, 2014.

[63] R. R. Varior, B. Shuai, J. Lu, D. Xu, and G. Wang. A
siamese long short-term memory architecture for human reidentification. In European Conference on Computer Vision,
pages 135–153. Springer, 2016.
[64] H. Wang, W. Wang, C. Zhang, and F. Xu. Cross-domain
metric learning based on information theory. In AAAI, pages
2099–2105, 2014.
[65] Z. Xu, W. Li, L. Niu, and D. Xu. Exploiting low-rank
structure from latent domains for domain generalization. In
ECCV, pages 628–643, 2014.
[66] J. Yang, R. Yan, and A. G. Hauptmann. Adapting svm classifiers to data with shifted distributions. In Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on, pages 69–76. IEEE, 2007.
[67] T. Yao, Y. Pan, C.-W. Ngo, H. Li, and T. Mei. Semisupervised domain adaptation with subspace learning for visual recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.

