EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders

Gulcin Baykal, Melih Kandemir, Gozde Unal

Keywords: Vector Quantized Variational Autoencoders; Discrete Variational Autoencoders; Evidential Deep Learning; Codebook Collapse

TL;DR: We mitigate the codebook collapse problem of the dVAEs by virtue of an evidential formulation.

Abstract: Codebook collapse is a common problem in training deep generative models with discrete representation spaces like Vector Quantized Variational Autoencoders (VQ-VAEs). We observe that the same problem arises for the alternatively designed discrete variational autoencoders (dVAEs) whose encoder directly learns a distribution over the codebook embeddings to represent the data. We hypothesize that using the softmax function to obtain a probability distribution causes the codebook collapse by assigning overconfident probabilities to the best matching codebook elements. In this paper, we propose a novel way to incorporate evidential deep learning (EDL) instead of softmax to combat the codebook collapse problem of dVAE. We evidentially monitor the significance of attaining the probability distribution over the codebook embeddings, in contrast to softmax usage. Our experiments using various datasets show that our model, called EdVAE, mitigates codebook collapse while improving the reconstruction performance, and enhances the codebook usage compared to dVAE and VQ-VAE based models.

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Submission Number: 5334