Apr 22, 2026 · cs.CLJ/K move · Enter open · S save
Wei Han, David Martinez, Anna Khanina, Lawrence Cavedon+1
School of Computing Technologies, RMIT University · Department of Infectious Disease, Peter MacCallum Cancer Centre · 3National Centre for Infections in Cancer, Melbourne · 5Sir Peter MacCallum Department of Oncology, The University of Melbourne · School of Computing and Information Systems, The University of Melbourne
A common strategy in transfer learning is few shot fine-tuning, but its success is highly dependent on the quality of samples selected as training examples. Active learning methods such as uncertainty sampling and diversity sampling can select useful samples. However, under extremely low-resource and class-imbalanced conditions, they often favor outliers rather than truly informative samples, resulting in degraded performance. In this paper, we introduce RADS (Reinforcement Adaptive Domain Sampling), a robust sample selection strategy using reinforcement learning (RL) to identify the most informative samples. Experimental evaluations on several real world clinical datasets show our sample selection strategy enhances model transferability while maintaining robust performance under extreme class imbalance compared to traditional methods.