Information Bottleneck and Aggregated Learning.

Area of Science:

• Machine Learning
• Computer Vision
• Natural Language Processing

Background:

• The information bottleneck (IB) principle offers a theoretical framework for representation learning in neural networks.
• Traditional IB learning focuses on individual object representations, potentially limiting classification performance.
• Connecting IB learning to quantization problems suggests opportunities for improved methods.

Purpose of the Study:

• To develop a novel neural network classification framework based on the information bottleneck principle.
• To explore the equivalence between IB learning and quantization problems.
• To introduce and validate the "Aggregated Learning" framework for enhanced classification.

Main Methods:

• Formulating representation learning as an IB learning problem.
• Establishing the equivalence between IB learning and a specific class of quantization problems.
• Applying vector quantization principles to jointly learn representations of multiple objects.
• Developing the "Aggregated Learning" framework using variational techniques.

Main Results:

• IB learning is shown to be equivalent to a specialized quantization problem.
• A novel "Aggregated Learning" framework is proposed, leveraging vector quantization for joint object classification.
• Extensive experiments demonstrate the effectiveness of Aggregated Learning on image recognition and text classification tasks.

Conclusions:

• Aggregated Learning provides an effective framework for neural network classification by jointly processing multiple objects.
• The connection between IB learning and vector quantization offers a new perspective on representation learning.
• The proposed method shows significant improvements in standard image and text classification benchmarks.