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》 资源 》论文》ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

2020-02-10 | |  50 |   41 |   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.

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