Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

TL;DR

This paper proposes the Dual-Balance Collaborative Experts framework to improve domain-incremental learning by addressing class imbalance and distribution shifts, achieving state-of-the-art results on benchmark datasets.

Contribution

It introduces a frequency-aware expert group and a dynamic expert selector with pseudo-feature synthesis to handle imbalance and domain shifts in continual learning.

Findings

DCE achieves superior performance on four benchmark datasets.

The frequency-aware expert group effectively mitigates intra-domain class imbalance.

Pseudo-feature synthesis enhances knowledge transfer across domains.

Abstract

Domain-Incremental Learning (DIL) focuses on continual learning in non-stationary environments, requiring models to adjust to evolving domains while preserving historical knowledge. DIL faces two critical challenges in the context of imbalanced data: intra-domain class imbalance and cross-domain class distribution shifts. These challenges significantly hinder model performance, as intra-domain imbalance leads to underfitting of few-shot classes, while cross-domain shifts require maintaining well-learned many-shot classes and transferring knowledge to improve few-shot class performance in old domains. To overcome these challenges, we introduce the Dual-Balance Collaborative Experts (DCE) framework. DCE employs a frequency-aware expert group, where each expert is guided by specialized loss functions to learn features for specific frequency groups, effectively addressing intra-domain class imbalance. Subsequently, a dynamic expert selector is learned by synthesizing pseudo-features through balanced Gaussian sampling from historical class statistics. This mechanism navigates the trade-off between preserving many-shot knowledge of previous domains and leveraging new data to improve few-shot class performance in earlier tasks. Extensive experimental results on four benchmark datasets demonstrate DCE's state-of-the-art performance.