{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:30:08Z","timestamp":1778081408203,"version":"3.51.4"},"reference-count":52,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100010428","name":"Innovation and Technology Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,1]]},"DOI":"10.1109\/iccv51070.2023.00152","type":"proceedings-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T15:55:59Z","timestamp":1705334159000},"page":"1579-1589","source":"Crossref","is-referenced-by-count":14,"title":["DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models"],"prefix":"10.1109","author":[{"given":"Ruiyuan","family":"Gao","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong"}]},{"given":"Chenchen","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}]},{"given":"Lanqing","family":"Hong","sequence":"additional","affiliation":[{"name":"Huawei Noah&#x2019;s Ark Lab"}]},{"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}]}],"member":"263","reference":[{"key":"ref1","article-title":"WAIC, but why? Generative ensembles for robust anomaly detection","author":"Choi","year":"2018"},{"key":"ref2","article-title":"Addressing failure prediction by learning model confidence","volume":"32","author":"Corbi\u00e8re","year":"2019","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref3","article-title":"Improving reconstruction autoencoder out-of-distribution detection with Mahalanobis distance","author":"Denouden","year":"2018"},{"key":"ref4","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3045810"},{"key":"ref6","first-page":"1","article-title":"The open world assumption","volume-title":"eSI Workshop: The Closed World of Databases meets the Open World of the Semantic Web","volume":"15","author":"Drummond"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00296"},{"key":"ref9","article-title":"On calibration of modern neural networks","volume-title":"Proceedings of The 34th International Conference on Machine Learning","author":"Guo"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref11","first-page":"8759","article-title":"Scaling out-of-distribution detection for real-world settings","volume-title":"International Conference on Machine Learning (ICML)","volume":"162","author":"Hendrycks"},{"key":"ref12","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"International Conference on Learning Representations (ICLR)","author":"Hendrycks"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01501"},{"key":"ref14","article-title":"Prompt-to-prompt image editing with cross attention control","author":"Hertz","year":"2022"},{"key":"ref15","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref16","article-title":"Classifier-free diffusion guidance","volume-title":"NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications","author":"Ho"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00860"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4206741"},{"key":"ref20","first-page":"20578","article-title":"Why normalizing flows fail to detect out-of-distribution data","volume":"33","author":"Kirichenko","year":"2020","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref21","author":"Krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref23","article-title":"Diffusion models already have a semantic latent space","volume-title":"International Conference on Learning Representations (ICLR)","author":"Kwon"},{"issue":"7","key":"ref24","first-page":"3","article-title":"Tiny ImageNet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"ref25","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","volume-title":"International Conference on Learning Representations (ICLR)","author":"Liang"},{"key":"ref26","article-title":"MagicMix: Semantic mixing with diffusion models","author":"Liew","year":"2022"},{"key":"ref27","author":"Liu","year":"2023","journal-title":"Out-of-distribution detection with diffusion-based neighborhood"},{"key":"ref28","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"Liu","year":"2020","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref29","article-title":"SDEdit: Guided image synthesis and editing with stochastic differential equations","volume-title":"International Conference on Learning Representations (ICLR)","author":"Meng"},{"key":"ref30","article-title":"Fake it until you make it: Towards accurate near-distribution novelty detection","volume-title":"International Conference on Learning Representations (ICLR)","author":"Mirzaei"},{"key":"ref31","article-title":"Do deep generative models know what they don\u2019t know?","volume-title":"International Conference on Learning Representations (ICLR)","author":"Nalisnick"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"ref33","first-page":"16784","article-title":"GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models","volume-title":"International Conference on Machine Learning (ICML)","volume":"162","author":"Nichol"},{"key":"ref34","article-title":"Generative probabilistic novelty detection with adversarial autoencoders","volume":"31","author":"Pidhorskyi","year":"2018","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref37","article-title":"SSD: A unified framework for self-supervised outlier detection","volume-title":"International Conference on Learning Representations (ICLR)","author":"Sehwag"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref39","article-title":"Input complexity and out-of-distribution detection with likelihood-based generative models","volume-title":"International Conference on Learning Representations (ICLR)","author":"Serr\u00e0"},{"key":"ref40","article-title":"Denoising diffusion implicit models","volume-title":"International Conference on Learning Representations (ICLR)","author":"Song"},{"key":"ref41","article-title":"Generative modeling by estimating gradients of the data distribution","volume":"32","author":"Song","year":"2019","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref42","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"International Conference on Learning Representations (ICLR)","author":"Song"},{"key":"ref43","article-title":"Building robust classifiers through generation of confident out of distribution examples","author":"Sricharan","year":"2018"},{"key":"ref44","first-page":"20827","article-title":"Out-of-distribution detection with deep nearest neighbors","volume-title":"International Conference on Machine Learning (ICML)","author":"Sun"},{"key":"ref45","first-page":"11839","article-title":"CSI: Novelty detection via contrastive learning on distributionally shifted instances","volume":"33","author":"Tack","year":"2020","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00487"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3181070"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2293423"},{"key":"ref49","article-title":"OpenOOD: Benchmarking generalized out-of-distribution detection","volume-title":"Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track","author":"Yang"},{"key":"ref50","article-title":"Generalized out-of-distribution detection: A survey","author":"Yang","year":"2021"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_22"},{"key":"ref52","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV51070.2023.00355","article-title":"Adding conditional control to text-to-image diffusion models","author":"Zhang","year":"2023"}],"event":{"name":"2023 IEEE\/CVF International Conference on Computer Vision (ICCV)","location":"Paris, France","start":{"date-parts":[[2023,10,1]]},"end":{"date-parts":[[2023,10,6]]}},"container-title":["2023 IEEE\/CVF International Conference on Computer Vision (ICCV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10376473\/10376477\/10376543.pdf?arnumber=10376543","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T20:48:31Z","timestamp":1705524511000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10376543\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,1]]},"references-count":52,"URL":"https:\/\/doi.org\/10.1109\/iccv51070.2023.00152","relation":{},"subject":[],"published":{"date-parts":[[2023,10,1]]}}}