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A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

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Disentangled Generation With Information Bottleneck for Enhanced Few-Shot Learning.

Zhuohang Dang, Minnan Luo, Jihong Wang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 30, 2024

Summary

Few-shot learning (FSL) generative models struggle with data scarcity and entangled outputs. DisGenIB, a novel framework using Information Bottleneck for disentangled generation, improves sample quality and classification performance.

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Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Few-shot learning (FSL) faces challenges classifying new classes due to limited data.
Existing generative FSL methods often produce entangled outputs, worsening distribution shifts and sample quality.

Purpose of the Study:

To introduce DisGenIB, a novel framework for disentangled generation in FSL.
To enhance sample discrimination and diversity while addressing data scarcity.

Main Methods:

Leveraging an Information Bottleneck (IB) approach for disentangled generation.
Developing a new Information Theoretic objective unifying representation learning and sample generation.
Incorporating priors as invariant domain knowledge to improve disentanglement.

Main Results:

DisGenIB effectively disentangles features, enhancing generated sample quality.
The framework demonstrates superior performance on demanding FSL benchmarks.
Theoretical analysis confirms prior methods as special cases of DisGenIB.

Conclusions:

DisGenIB offers a robust solution for FSL by improving generative model disentanglement and sample quality.
The framework's ability to leverage priors enhances its versatility and effectiveness.
Experimental validation supports the theoretical underpinnings and practical efficacy of DisGenIB.