Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer.

Area of Science:

• Machine Learning
• Computer Vision
• Natural Language Processing

Background:

• Severe class imbalance is a significant challenge in real-world machine learning applications.
• Accurate classification and generalization of minority classes are often critical.
• Existing methods rely on sampling or weighting adjustments, or inductive bias.

Purpose of the Study:

• To propose a novel perspective for promoting sample efficiency and model generalization using causality.
• To enable efficient knowledge transfer from dominant to minority classes despite feature disparities.
• To develop a causal data augmentation procedure for minority class representation.

Main Methods:

• Positing a meta-distributional scenario based on causal invariance principles.
• Assuming invariance in the causal generating mechanism for label-conditional features across labels.
• Developing a causal data augmentation technique.

Main Results:

• Demonstrated efficient knowledge transfer from majority to minority classes.
• Successfully enlarged the representation of minority classes through causal augmentation.
• Validated the approach on diverse synthetic and real-world datasets.

Conclusions:

• The proposed causal framework offers a novel solution for imbalanced learning.
• This method is orthogonal to existing techniques and can be seamlessly integrated.
• The approach enhances model generalization and sample efficiency for minority classes.