3 papers
cs.LG2026
Residual SODAP: Residual Self-Organizing Domain-Adaptive Prompting with Structural Knowledge Preservation for Continual Learning
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…
cs.LG2025
UniPrompt-CL: Sustainable Continual Learning in Medical AI with Unified Prompt Pools
Gyutae Oh, Jitae Shin
Modern AI models are typically trained on static datasets, limiting their ability to continuously adapt to rapidly evolving real-world environments. While continual learning (CL) a…
cs.CV2024
Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets
Hyekyoung Hwang, Jitae Shin
Deep Learning (DL) has made remarkable achievements in computer vision and adopted in safety critical domains such as medical imaging or autonomous drive. Thus, it is necessary to…