DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models

TL;DR

DiffGuard leverages pre-trained diffusion models to enhance semantic mismatch detection for out-of-distribution images, achieving state-of-the-art results on large-scale datasets like ImageNet.

Contribution

This work introduces DiffGuard, a novel OOD detection method using diffusion models to improve semantic mismatch detection, overcoming cGAN training limitations.

Findings

Effective on CIFAR-10 and ImageNet datasets.

Can be combined with existing OOD detection techniques.

Achieves state-of-the-art results on large-scale datasets.

Abstract

Given a classifier, the inherent property of semantic Out-of-Distribution (OOD) samples is that their contents differ from all legal classes in terms of semantics, namely semantic mismatch. There is a recent work that directly applies it to OOD detection, which employs a conditional Generative Adversarial Network (cGAN) to enlarge semantic mismatch in the image space. While achieving remarkable OOD detection performance on small datasets, it is not applicable to ImageNet-scale datasets due to the difficulty in training cGANs with both input images and labels as conditions. As diffusion models are much easier to train and amenable to various conditions compared to cGANs, in this work, we propose to directly use pre-trained diffusion models for semantic mismatch-guided OOD detection, named DiffGuard. Specifically, given an OOD input image and the predicted label from the classifier, we try to enlarge the semantic difference between the reconstructed OOD image under these conditions and the original input image. We also present several test-time techniques to further strengthen such differences. Experimental results show that DiffGuard is effective on both Cifar-10 and hard cases of the large-scale ImageNet, and it can be easily combined with existing OOD detection techniques to achieve state-of-the-art OOD detection results.