Improving disentanglement in variational auto-encoders via feature imbalance-informed dimension weighting-Reference-Cited by-同舟云学术

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Improving disentanglement in variational auto-encoders via feature imbalance-informed dimension weighting

Published:2024-07 Issue: Volume:296 Page:111818
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Liu Yue^ORCID,Yu Zhenyao,Liu Zitu,Yu Ziyi,Yang Xinyan,Li Xingyue,Guo Yike,Liu Qun,Wang Guoyin^ORCID

Funder

Chongqing Municipal Education Commission

National Natural Science Foundation of China

Natural Science Foundation of Chongqing Municipality

Publisher

Elsevier BV

Reference52 articles.

1. Representation learning: A review and new perspectives;Bengio;IEEe Trans. Pattern. Anal. Mach. Intell.,2013

2. Generalized zero-shot learning via disentangled representation;Li;Proc. AAAI Conf. Artificial Intell.,2021

3. Where and what? Examining interpretable disentangled representations;Zhu,2021

4. Learning disentangled representations via independent subspaces;Awiszus,2019

5. Infogan: Interpretable representation learning by information maximizing generative adversarial nets;Chen,2016

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