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This study introduces a novel semi-supervised classification framework using the information bottleneck (IB) principle. It enhances understanding of regularizers

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Information Theory

Background:

  • Semi-supervised classification leverages limited labeled data alongside abundant unlabeled data.
  • The Information Bottleneck (IB) theory provides a framework for understanding representation learning by compressing input data while preserving relevant information for a target task.
  • Variational methods are commonly used to approximate intractable distributions in machine learning models.

Purpose of the Study:

  • To develop a novel semi-supervised classification framework based on the Information Bottleneck (IB) principle.
  • To analyze the impact of different regularizers and variational model components on classification accuracy.
  • To provide a unified perspective on existing semi-supervised methods within the IB framework.

Main Methods:

  • Application of a variational decomposition of mutual information terms within the IB framework.
  • Development of a new semi-supervised IB formulation incorporating hand-crafted and learnable priors.
  • Comparison and linkage of the proposed method to existing semi-supervised models like VAE, AAE, and CatGAN.

Main Results:

  • Demonstration of the practical impact of various regularizers and variational model components on classification performance.
  • Establishment of a connection between the proposed IB model and established semi-supervised techniques.
  • Experimental validation on the MNIST dataset, showcasing performance across varying amounts of labeled data.

Conclusions:

  • The proposed Information Bottleneck (IB) framework offers a unified approach to understanding semi-supervised classification.
  • The model provides insights into the role of different regularizers in enhancing classification accuracy.
  • The framework, incorporating hand-crafted and learnable priors, demonstrates effectiveness in semi-supervised learning tasks.