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Computer Science > Machine Learning

arXiv:2109.14795 (cs)
[Submitted on 30 Sep 2021 (v1), last revised 16 Aug 2022 (this version, v2)]

Title:Towards Better Data Augmentation using Wasserstein Distance in Variational Auto-encoder

Authors:Zichuan Chen, Peng Liu
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Abstract: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.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.14795 [cs.LG]
  (or arXiv:2109.14795v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.14795

Submission history

From: Peng Liu [view email]
[v1] Thu, 30 Sep 2021 01:40:39 UTC (389 KB)
[v2] Tue, 16 Aug 2022 01:38:20 UTC (500 KB)
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