Enhancing OOD Detection Using Latent Diffusion

Abstract: Numerous Out-of-Distribution (OOD) detection algorithms have been developed to identify unknown samples or objects in real-world deployments. One line of work related to OOD detection propose utilizing auxiliary datasets to train OOD detectors, thereby enhancing the performance of OOD detection. Recently, researchers propose to leverage Stable Diffusion (SD) to generate outliers in the pixel space, which may complicate network training. To mitigate this issue, we propose an Outlier Aware Learning (OAL) framework, which synthesizes OOD training data in the latent space. This improvement enables us to train the network with only a few synthesized outliers. Besides, to regularize the model's decision boundary, we develop a mutual information-based contrastive learning module (MICL) that amplifies the distinction between In-Distribution (ID) and collected OOD features. Moreover, we develop a knowledge distillation module to prevent the degradation of ID classification accuracy when training with OOD data. Extensive experiments on CIFAR-10/100 benchmarks demonstrate the superior performance of our method.