Variational Distillation for Multi-View Learning.

Area of Science:

• Machine Learning
• Information Theory
• Computer Vision

Background:

• Information Bottleneck (IB) offers a principle for multi-view learning, aiming for view-invariance and predictive representations.
• Existing methods struggle with the complexity of mutual information (MI) terms, especially for generalized multi-view learning.
• Sufficiency and consistency are key roles in multi-view representation learning, but current variational distillation frameworks face challenges with arbitrary viewpoints.

Purpose of the Study:

• To address the limitations of existing methods in generalized multi-view learning.
• To develop a scalable solution for achieving both sufficiency and consistency in representations.
• To rigorously reformulate the Information Bottleneck objective for improved MI optimization.

Main Methods:

• Introducing Multi-View Variational Distillation (MV2D), a novel framework for generalized multi-view learning.
• MV2D recognizes useful consistent information and prioritizes diverse components based on their generalization ability.
• The method analytically reformulates the IB objective to overcome challenges in MI optimization.

Main Results:

• MV2D provides an analytical and scalable solution for achieving sufficiency and consistency.
• The model effectively manages complex mutual information terms in generalized multi-view learning.
• Extensive evaluations demonstrate considerable gains across diverse tasks, validating the model's effectiveness.

Conclusions:

• MV2D successfully tackles limitations in generalized multi-view learning by leveraging information-theoretic principles.
• The framework offers key insights into creating generalized multi-view representations.
• MV2D fully realizes the theoretical advantages of the Information Bottleneck principle.