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

• URL: http://arxiv.org/abs/2308.07687v2
• Date: Wed, 16 Aug 2023 05:24:46 GMT
• Title: DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models
• Authors: Ruiyuan Gao, Chenchen Zhao, Lanqing Hong, Qiang Xu
• Abstract summary: We use pre-trained diffusion models for semantic mismatch-guided OOD detection, named DiffGuard. Experiments show that DiffGuard is effective on both Cifar-10 and hard cases of the large-scale ImageNet. It can be easily combined with existing OOD detection techniques to achieve state-of-the-art OOD detection results.
• Score: 25.58447344260747
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models. In this paper, we investigate how detection performance varies across model backbones, types, and datasets. We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector [25.224930928724326]
  Out-of-Distribution (OOD) detection is crucial when deploying machine learning models in open-world applications. We propose a novel OOD detection framework FodFoM that innovatively combines multiple foundation models to generate two types of challenging fake outlier images. New state-of-the-art OOD detection performance is achieved on multiple benchmarks.
  arXiv  Detail & Related papers  (2024-11-22T17:29:52Z)
• Can Your Generative Model Detect Out-of-Distribution Covariate Shift? [2.0144831048903566]
  We propose a novel method for detecting Out-of-Distribution (OOD) sensory data using conditional Normalizing Flows (cNFs) Our results on CIFAR10 vs. CIFAR10-C and ImageNet200 vs. ImageNet200-C demonstrate the effectiveness of the method.
  arXiv  Detail & Related papers  (2024-09-04T19:27:56Z)
• Exploiting Diffusion Prior for Out-of-Distribution Detection [11.11093497717038]
  Out-of-distribution (OOD) detection is crucial for deploying robust machine learning models. We present a novel approach for OOD detection that leverages the generative ability of diffusion models and the powerful feature extraction capabilities of CLIP.
  arXiv  Detail & Related papers  (2024-06-16T23:55:25Z)
• Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox [70.57120710151105]
  Most existing out-of-distribution (OOD) detection benchmarks classify samples with novel labels as the OOD data. Some marginal OOD samples actually have close semantic contents to the in-distribution (ID) sample, which makes determining the OOD sample a Sorites Paradox. We construct a benchmark named Incremental Shift OOD (IS-OOD) to address the issue.
  arXiv  Detail & Related papers  (2024-06-14T09:27:56Z)
• A noisy elephant in the room: Is your out-of-distribution detector robust to label noise? [49.88894124047644]
  We take a closer look at 20 state-of-the-art OOD detection methods. We show that poor separation between incorrectly classified ID samples vs. OOD samples is an overlooked yet important limitation of existing methods.
  arXiv  Detail & Related papers  (2024-04-02T09:40:22Z)
• From Global to Local: Multi-scale Out-of-distribution Detection [129.37607313927458]
  Out-of-distribution (OOD) detection aims to detect "unknown" data whose labels have not been seen during the in-distribution (ID) training process. Recent progress in representation learning gives rise to distance-based OOD detection. We propose Multi-scale OOD DEtection (MODE), a first framework leveraging both global visual information and local region details.
  arXiv  Detail & Related papers  (2023-08-20T11:56:25Z)
• Masked Images Are Counterfactual Samples for Robust Fine-tuning [77.82348472169335]
  Fine-tuning deep learning models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. We propose a novel fine-tuning method, which uses masked images as counterfactual samples that help improve the robustness of the fine-tuning model.
  arXiv  Detail & Related papers  (2023-03-06T11:51:28Z)
• OODformer: Out-Of-Distribution Detection Transformer [15.17006322500865]
  In real-world safety-critical applications, it is important to be aware if a new data point is OOD. This paper proposes a first-of-its-kind OOD detection architecture named OODformer.
  arXiv  Detail & Related papers  (2021-07-19T15:46:38Z)
• Why Normalizing Flows Fail to Detect Out-of-Distribution Data [51.552870594221865]
  Normalizing flows fail to distinguish between in- and out-of-distribution data. We demonstrate that flows learn local pixel correlations and generic image-to-latent-space transformations. We show that by modifying the architecture of flow coupling layers we can bias the flow towards learning the semantic structure of the target data.
  arXiv  Detail & Related papers  (2020-06-15T17:00:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.