Researcher, Apple
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.
The paper introduces a novel latent diffusion model that uses local vicinity structures to achieve state-of-the-art domain adaptation, preserving privacy without needing source data during adaptation.
