Vector Quantized Wasserstein Auto-Encoder
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 main...
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Published in | arXiv.org |
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Main Authors | , , , , , , |
Format | Paper |
Language | English |
Published |
Ithaca
Cornell University Library, arXiv.org
17.06.2023
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Subjects | |
Online Access | Get full text |
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