Vector Quantized Wasserstein Auto-Encoder

                                                            @inproceedings{10.5555/3618408.3619875,
author = {Vuong, Tung-Long and Le, Trung and Zhao, He and Zheng, Chuanxia and Harandi, Mehrtash and Cai, Jianfei and Phung, Dinh},
title = {Vector quantized Wasserstein auto-encoder},
year = {2023},
publisher = {JMLR.org},
abstract = {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.},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
articleno = {1467},
numpages = {20},
location = {<conf-loc>, <city>Honolulu</city>, <state>Hawaii</state>, <country>USA</country>, </conf-loc>},
series = {ICML'23}
}