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

• URL: http://arxiv.org/abs/2507.07100v1
• Date: Wed, 09 Jul 2025 17:57:07 GMT
• Title: Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
• Authors: Lan Li, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan,
• Abstract summary: Domain-Incremental Learning (DIL) focuses on continual learning in non-stationary environments.<n>DIL faces two critical challenges in the context of imbalanced data: intra-domain class imbalance and cross-domain class distribution shifts.<n>We introduce the Dual-Balance Collaborative Experts (DCE) framework to overcome these challenges.
• Score: 59.615381619866284
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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