Vector Quantized Wasserstein Auto-Encoder
This work addresses the challenge of learning discrete representations for generative tasks, offering a novel approach that enhances control and performance in clustering and generation, though it is incremental relative to existing VQ-VAE methods.
The paper tackles the problem of learning deep discrete latent representations from a generative viewpoint, proposing a method that achieves better qualitative and quantitative performance than VQ-VAE variants, with improvements in codebook utilization and image reconstruction/generation on several benchmarks.
Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks. Inspired by the seminal Vector Quantized Variational Auto-Encoder (VQ-VAE), most of work in learning deep discrete representations has mainly focused on improving the original VQ-VAE form and none of them has studied learning deep discrete representations from the generative viewpoint. In this work, we study learning deep discrete representations from the generative viewpoint. Specifically, we endow discrete distributions over sequences of codewords and learn a deterministic decoder that transports the distribution over the sequences of codewords to the data distribution via minimizing a WS distance between them. We develop further theories to connect it with the clustering viewpoint of WS distance, allowing us to have a better and more controllable clustering solution. Finally, we empirically evaluate our method on several well-known benchmarks, where it achieves better qualitative and quantitative performances than the other VQ-VAE variants in terms of the codebook utilization and image reconstruction/generation.