Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need (2505.23744v1)

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.

An Analysis of Boosting Domain Incremental Learning Through Optimal Parameter Selection

The paper "Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need" presents a significant advancement in Domain Incremental Learning (DIL), especially focusing on improving parameter selection accuracy in varying domains encountered in realistic, dynamic environments. The authors introduce SOYO, a framework designed to enhance Domain Incremental Learning by addressing the challenges of parameter selection in the context of Parameter-Isolation Domain Incremental Learning (PIDIL).

Overview of the Problem and Approach

Deep neural networks (DNNs) often face substantial challenges in environments where data distributions evolve over time, such as autonomous driving under different weather conditions. Traditional approaches in DIL, including knowledge distillation and parameter regularization, offer solutions but often suffer from catastrophic forgetting, where models ineffectively balance the learning of new data and the retention of previously acquired knowledge. PIDIL presents a paradigm shift by deploying separate parameters for different domains to mitigate knowledge conflicts. Nonetheless, the accuracy of parameter selection remains a bottleneck due to increasing complexity with more domains and classes.

SOYO addresses these challenges through its lightweight framework, composed of three main components: the Gaussian Mixture Compressor (GMC), the Domain Feature Resampler (DFR), and the Multi-level Domain Feature Fusion Network (MDFN). The GMC efficiently compresses past domain features, reducing memory overhead while preserving critical data aspects. The DFR reconstructs pseudo-domain features, balancing training on imbalanced datasets without compromising privacy or memory. Finally, the MDFN leverages multi-level feature fusion to extract more discriminative domain features, crucial for enhancing parameter selection accuracy during inference.

Empirical Results and Interpretation

The effectiveness of SOYO is validated across various tasks including image classification, object detection, and speech enhancement, using benchmarks such as DomainNet, CORe50, Pascal VOC series, and WSJ0 synthetic datasets. Results consistently show SOYO's superiority over existing baselines, with improvements of up to 19.6% in parameter selection accuracy. In DIC tasks, SOYO enhances average accuracy while reducing forgetting significantly—demonstrating robustness and adaptability across both overlapping and non-overlapping domains.

For DIOD, SOYO achieves near-oracle performance, bridging the gap between theoretical and practical implementations by offering significant gains in mean average precision (mAP). In DISE tasks, metrics such as SI-SNR, SDR, and PESQ are improved, showcasing SOYO’s versatility in handling dynamic audio environments.

Theoretical and Practical Implications

The implications of this research extend beyond immediate performance improvements. The ability to accurately select parameters across evolving domains reduces the computational overhead traditionally associated with PIDIL methods. The integration of components like GMC and DFR reflects a balance between efficient data compression and effective domain representation, addressing both storage concerns and privacy. Additionally, MDFN's design choices highlight the necessity of leveraging both shallow and deep features—a principle that can be generalized to other model architectures in AI applications.

Speculation on Future Developments

Looking forward, the framework introduced by SOYO may inspire a new wave of domain adaptation research where parameter selection accuracy plays a more central role. Enhanced accuracy in domains can lead to more personalized and context-aware applications, particularly in areas where AI systems interact with variable environments such as robotics, healthcare, and intelligent transport. The modularity of SOYO suggests potential adaptability and scalability in its components, allowing for further refinements based on domain-specific needs or emerging model architectures.

In summary, the paper presents a comprehensive framework for advancing Domain Incremental Learning, achieving significant strides in accuracy, efficiency, and adaptability. SOYO not only addresses current challenges in parameter selection but also lays the groundwork for future explorations in continual learning, domain adaptation, and real-world AI applications.