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...

全面介紹

Saved in:
書目詳細資料
Main Authors: CHEN, Zichuan, LIU, Peng
格式: text
語言:English
出版: Institutional Knowledge at Singapore Management University 2021
主題:
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
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!

相似書籍