Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need

• URL: http://arxiv.org/abs/2505.23744v1
• Date: Thu, 29 May 2025 17:58:57 GMT
• Title: Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
• Authors: Qiang Wang, Xiang Song, Yuhang He, Jizhou Han, Chenhao Ding, Xinyuan Gao, Yihong Gong,
• Abstract summary: Domain Incremental Learning (DIL) offers a solution by enabling continual model adaptation.<n>Existing PIDIL methods struggle with parameter selection accuracy as the number of domains and corresponding classes grows.<n>We propose SOYO, a lightweight framework that improves domain selection in PIDIL.
• Score: 25.45880299215022
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep neural networks (DNNs) often underperform in real-world, dynamic settings where data distributions change over time. Domain Incremental Learning (DIL) offers a solution by enabling continual model adaptation, with Parameter-Isolation DIL (PIDIL) emerging as a promising paradigm to reduce knowledge conflicts. However, existing PIDIL methods struggle with parameter selection accuracy, especially as the number of domains and corresponding classes grows. To address this, we propose SOYO, a lightweight framework that improves domain selection in PIDIL. SOYO introduces a Gaussian Mixture Compressor (GMC) and Domain Feature Resampler (DFR) to store and balance prior domain data efficiently, while a Multi-level Domain Feature Fusion Network (MDFN) enhances domain feature extraction. Our framework supports multiple Parameter-Efficient Fine-Tuning (PEFT) methods and is validated across tasks such as image classification, object detection, and speech enhancement. Experimental results on six benchmarks demonstrate SOYO's consistent superiority over existing baselines, showcasing its robustness and adaptability in complex, evolving environments. The codes will be released in https://github.com/qwangcv/SOYO.

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