mlsanityData to 7 Oct 2026

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

arXiv 1709.01215 ↗First posted 5 Sep 2017stat.ML cs.AI cs.CVcited by 32

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Abstract (arXiv)

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.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2017.

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Citing papers per year: 2021: 16; 2022: 6; 2023: 5; 2024: 2; 2025: 2; 2026: 1.

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