Contrastive Predictive Coding Done Right for Mutual Information Estimation
J. Jon Ryu; Pavan Yeddanapudi; Xiangxiang Xu; Gregory W. Wornell · 2025 · arXiv
WASTE classifies this as Replication Failure · AI classification, approximate
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Abstract (excerpt)
The InfoNCE objective, originally introduced for contrastive representation learning, has become a popular choice for mutual information (MI) estimation, despite its indirect connection to MI. In this paper, we demonstrate why InfoNCE should not be regarded as a valid MI estimator, and we introduce a simple modification, which we refer to as InfoNCE-anchor, for accurate MI estimation. Our modification introduces an auxiliary anchor class, enabling consistent density ratio estimation and yielding a plug-in MI estimator with significantly reduced bias. Beyond this, we generalize our framework us
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Metadata source: arXiv