Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure

arXiv:2405.00631v1 Announce Type: new
Abstract: In this paper, we present a novel approach that combines deep metric learning and synthetic data generation using diffusion models for out-of-distribution (OOD) detection. One popular approach for OOD detection is outlier exposure, where models are trained using a mixture of in-distribution (ID) samples and ``seen" OOD samples. For the OOD samples, the model is trained to minimize the KL divergence between the output probability and the uniform distribution while correctly classifying the in-distribution (ID) …

abstract arxiv cs.cv data data generation detection diffusion diffusion models distribution novel outlier paper popular samples synthetic synthetic data type