Towards better data augmentation using Wasserstein distance in variational auto-encoder
VAE, or variational auto-encoder, compresses data into latent attributes, and generates new data of different varieties. VAE based on KL divergence has been considered as an effective technique for data augmentation. In this paper, we propose the use of Wasserstein distance as a measure of distribut...
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| 格式: | text |
| 語言: | English |
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Institutional Knowledge at Singapore Management University
2021
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| 在線閱讀: | https://ink.library.smu.edu.sg/lkcsb_research/7046 https://ink.library.smu.edu.sg/context/lkcsb_research/article/8045/viewcontent/2109.14795.pdf |
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