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.

提出了一种名为 EdVAE 的模型，通过使用证据深度学习（EDL）来解决 dVAE 的代码书坍塌问题，同时提高重建性能和代码书使用率。

EdVAE：使用证据离散变分自动编码器缓解码书塌陷

EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational  Autoencoders

The success of deep neural nets heavily relies on their ability to encode
complex relations between their input and their output. While this property
serves to fit the training data well, it also obscures the mechanism that
drives prediction. This study aims to reveal hidden concepts by employing an
intervention mechanism that shifts the predicted class based on discrete
variational autoencoders. An explanatory model then visualizes the encoded
information from any hidden layer and its corresponding intervened
representation. By the assessment of differences between the original
representation and the intervened representation, one can determine the
concepts that can alter the class, hence providing interpretability. We
demonstrate the effectiveness of our approach on CelebA, where we show various
visualizations for bias in the data and suggest different interventions to
reveal and change bias.

本研究通过使用干预机制，利用离散变分自编码器来转移预测类别，进而可视化任何隐藏层的编码信息和对应的被干预表示来揭示深度神经网络的隐含概念，并通过评估原始表示与被干预表示之间的差异来确定可改变类别的概念，从而提高模型的可解释性。我们在 CelebA 数据集上展示了我们方法的有效性，并展示了有偏差数据的各种可视化并提供不同的干预方法来揭示和更正偏见。