MS student, Yonsei University
2 papers at NeurIPS 2025
A two‐stage active learning pipeline that uses diffusion‐based feature sampling and entropy‐augmented disagreement to pick the most informative pixels under extreme labeling constraints.
Our Orthogonal Residual Update improves deep networks by adding only novel, stream-orthogonal module outputs, boosting generalization, stability, and efficiency.
