ConceptVAE learns to discretize echocardiograms into fine-grained anatomical concepts and per-concept styles without labels, and reports gains over a VICReg baseline on retrieval, segmentation, and OOD detection.
Back to the Basics: Revisiting Out-of-Distribution Detection Baselines
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abstract
We study simple methods for out-of-distribution (OOD) image detection that are compatible with any already trained classifier, relying on only its predictions or learned representations. Evaluating the OOD detection performance of various methods when utilized with ResNet-50 and Swin Transformer models, we find methods that solely consider the model's predictions can be easily outperformed by also considering the learned representations. Based on our analysis, we advocate for a dead-simple approach that has been neglected in other studies: simply flag as OOD images whose average distance to their K nearest neighbors is large (in the representation space of an image classifier trained on the in-distribution data).
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ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies
ConceptVAE learns to discretize echocardiograms into fine-grained anatomical concepts and per-concept styles without labels, and reports gains over a VICReg baseline on retrieval, segmentation, and OOD detection.