ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Chunyuan Li, Hao liu, Changyou Chen, Yunchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin
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- github.com/ChunyuanLI/ALICEOfficialIn papertf★ 0
- github.com/weituo12321/PREVALENTnone★ 95
- github.com/FilLTP89/SeismoALICEpytorch★ 0
- github.com/zhenxuan00/graphical-gantf★ 0
- github.com/ChunyuanLI/MNIST_Inception_Scoretf★ 0
Abstract
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.