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arXiv Machine Learning By Jingyao Zhang, Yuxuan Li, Lu Han, Ali Anaissi, Nguyen H. Tran

Rethinking the Information Bottleneck: Structured Decomposition under Label-Induced Partitions

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The paper proposes a structured version of the Information Bottleneck (IB) that separates label-relevant structure from within-condition variation using a dual-bottleneck formulation. It introduces a conditional KL term that targets within-condition information, allowing explicit control over nuisance-like variation in learned representations. Experiments demonstrate improved performance in low-data classification and consistent gains on dense prediction tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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