SetFlow: Generating Structured Sets of Representations for Multiple Instance Learning

This work introduces SetFlow, a generative architecture that models entire Multiple Instance Learning (MIL) bags directly in the representation space. It leverages the flow matching paradigm and a Set Transformer-inspired design to capture intra-bag dependencies and generate coherent, semantically consistent representations.