Multi-view Information Bottleneck Without Variational Approximation

Code

• github.com/archy666/meib
  OfficialIn paperpytorch★ 5

Abstract

By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks. In this work, we extend the information bottleneck principle to a supervised multi-view learning scenario and use the recently proposed matrix-based R\'enyi's -order entropy functional to optimize the resulting objective directly, without the necessity of variational approximation or adversarial training. Empirical results in both synthetic and real-world datasets suggest that our method enjoys improved robustness to noise and redundant information in each view, especially given limited training samples. Code is available at~https://github.com/archy666/MEIB.

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