Gyutae Oh

Gyutae Oh, Jungwoo Bae, Jitae Shin

Continual learning (CL) suffers from catastrophic forgetting, which is exacerbated in domain-incremental learning (DIL) where task identifiers are unavailable and storing past data is infeasible. While prompt-based CL (PCL) adapts representations with a frozen backbone, we observe that prompt-only improvements are often insufficient due to suboptimal prompt selection and classifier-level instability under domain shifts. We propose Residual SODAP, which jointly performs prompt-based representation adaptation and classifier-level knowledge preservation. Our framework combines $α$-entmax sparse prompt selection with residual aggregation, data-free distillation with pseudo-feature replay, prompt-usage--based drift detection, and uncertainty-aware multi-loss balancing. Across three DIL benchmarks without task IDs or extra data storage, Residual SODAP achieves state-of-the-art AvgACC/AvgF of 0.850/0.047 (DR), 0.760/0.031 (Skin Cancer), and 0.995/0.003 (CORe50).

Gyutae Oh, Jitae Shin

Modern AI models achieve state-of-the-art performance with large-scale, high-quality datasets; however, ethical, social, and institutional constraints in the medical domain severely restrict data sharing, rendering centralized learning nearly impossible. Each institution must incrementally update models using only local data. Traditional training overfits new samples and suffers from catastrophic forgetting, losing previously acquired knowledge. Medical data distributions also shift due to varying diagnostic equipment and demographics. Although continual learning (CL) has advanced, most methods address natural images, leaving medical-domain-specific CL underexplored. We propose a prompt-based continual learning (PCL) approach featuring a unified prompt pool with a minimal expansion strategy: by expanding and freezing a subset of prompts, our method reduces computational overhead, and a novel regularization term balances retention and adaptation. Experiments on three diabetic retinopathy datasets Aptos2019, LI2019, and Diabetic Retinopathy Detection show our model improves final classification accuracy by at least 10% and F1-score by 9 points over state-of-the-art approaches while lowering inference cost. We anticipate this study will drive sustainable medical AI advances, enabling real-time diagnosis, patient monitoring, and telemedicine applications in distributed healthcare. Code will be released upon acceptance