Title: Robust Cross-Modal Semi-supervised Few Shot Learning. Abstract: Semi-supervised learning has been successfully applied to few-shot
learning (FSL) due to its capability of leveraging the information
of limited labeled data and massive unlabeled data. However, in many
realistic applications, the query and support sets provided for FSL
are potentially noisy or unreadable where the noise exists in both
corrupted labels and outliers. Motivated by that, we propose to
employ a robust cross-modal semi-supervised few-shot learning
(RCFSL) based on Bayesian deep learning. By placing the uncertainty
prior on top of the parameters of infinite Gaussian mixture model
for noisy input, multi-modality information from image and text data
are integrated into a robust heterogenous variational autoencoder.
Subsequently, a robust divergence measure is employed to further
enhance the robustness, where a novel variational lower bound is
derived and optimized to infer the network parameters. Finally, a robust semi-supervised
generative adversarial network is employed to generate robust
features to compensate data sparsity in few shot learning and a
joint optimization is applied for training and inference. Our
approach is more parameter-efficient, scalable and adaptable
compared to previous approaches. Superior performances over the
state-of-the-art on multiple benchmark multi-modal dataset are
demonstrated given the complicated noise for semi-supervised
few-shot learning.