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.
authors: Xu Chen
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