Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Better Data Augmentation using Wasserstein Distance in
  Variational Auto-encoder   VAE, or variational auto-encoder, compresses data into latent attributes, and
generates new data of different varieties. VAE based on KL divergence has been
considered as an effective technique for data augmentation. In this paper, we
propose the use of Wasserstein distance as a measure of distributional
similarity for the latent attributes, and show its superior theoretical lower
bound (ELBO) compared with that of KL divergence under mild conditions. Using
multiple experiments, we demonstrate that the new loss function exhibits better
convergence property and generates artificial images that could better aid the
image classification tasks.

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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
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The categories of this paper are: eess
31
The categories of this paper are: ao-sci