Hoeffding Concept Bottleneck Models with Applications to Overhead Images

In three linesHoeffding Concept Bottleneck Models (HCBM) replace linear concept aggregation with non-linear sparse decomposition based on gradient-boosted trees. Robust to inter-concept leakage, they improve explainability of computer vision predictions, particularly on overhead imagery.

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