Title: Few-Shot Learning with Simplex. Abstract: Deep learning has made remarkable achievement in many fields. However, learning
the parameters of neural networks usually demands a large amount of labeled
data. The algorithms of deep learning, therefore, encounter difficulties when applied
to supervised learning where only little data are available. This specific task
is called few-shot learning. To address it, we propose a novel algorithm for fewshot
learning using discrete geometry, in the sense that the samples in a class are
modeled as a reduced simplex. The volume of the simplex is used for the measurement
of class scatter. During testing, combined with the test sample and the
points in the class, a new simplex is formed. Then the similarity between the test
sample and the class can be quantized with the ratio of volumes of the new simplex
to the original class simplex. Moreover, we present an approach to constructing
simplices using local regions of feature maps yielded by convolutional neural networks.
Experiments on Omniglot and miniImageNet verify the effectiveness of
our simplex algorithm on few-shot learning.