SetFlow: Generating Structured Sets of Representations for Multiple Instance Learning
Data scarcity and weak supervision continue to limit the performance of machine learning models in many real-world applications, such as mammography, where Multiple Instance Learning (MIL) often offers the best formulation. While recent foundation models provide strong semantic representations out of the box, effective augmentation of such representations of MIL data remains limited, as existing methods operate at the instance level and fail to capture intra-bag dependencies. In this work, we introduce SetFlow, a generative architecture that models entire MIL bags (i.e., sets) directly in the representation space. Our approach leverages the flow matching paradigm combined with a Set Trans...
| Authors | Nikola Jovišić, Milica Škipina, Vanja Švenda |
|---|---|
| Primary category | cs.LG |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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