How Low Can You Go? Surfacing Prototypical In-Distribution Samples for
Unsupervised Anomaly Detection

How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection

6 December 2023

Johannes Getzner

Alexander Ziller

Georgios Kaissis

Daniel Rueckert

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Papers citing "How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection"

16 / 16 papers shown

Title
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art I. Lagogiannis Felix Meissen Georgios Kaissis Daniel Rueckert OOD 123 20 0 01 Mar 2023
Unsupervised Anomaly Localization with Structural Feature-Autoencoders Felix Meissen Johannes C. Paetzold Georgios Kaissis Daniel Rueckert MedIm 44 17 0 23 Aug 2022
Towards Total Recall in Industrial Anomaly Detection Karsten Roth Latha Pemula J. Zepeda Bernhard Schölkopf Thomas Brox Peter V. Gehler UQCV 88 919 0 15 Jun 2021
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization Thomas Defard Aleksandr Setkov Angélique Loesch Romaric Audigier UQCV 79 843 0 17 Nov 2020
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation Tal Reiss Niv Cohen Liron Bergman Yedid Hoshen 69 252 0 12 Oct 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 199 470 0 09 Aug 2020
Classification-Based Anomaly Detection for General Data Liron Bergman Yedid Hoshen 52 351 0 05 May 2020
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection Jianpeng Zhang Yutong Xie Guansong Pang Zhibin Liao Johan Verjans ... Zongji Sun Jian He Yi Li Chunhua Shen Yong-quan Xia 53 446 0 27 Mar 2020
Deep Nearest Neighbor Anomaly Detection Liron Bergman Niv Cohen Yedid Hoshen UQCV 82 160 0 24 Feb 2020
Towards Visually Explaining Variational Autoencoders Wenqian Liu Runze Li Meng Zheng Srikrishna Karanam Ziyan Wu B. Bhanu Richard J. Radke Mario Sznaier 103 219 0 18 Nov 2019
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings Paul Bergmann Michael Fauser David Sattlegger C. Steger 84 665 0 06 Nov 2019
Unsupervised Anomaly Localization using Variational Auto-Encoders David Zimmerer Fabian Isensee Jens Petersen Simon A. A. Kohl Klaus Maier-Hein DRL 65 135 0 04 Jul 2019
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations Pramuditha Perera Ramesh Nallapati Bing Xiang 118 526 0 20 Mar 2019
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison Jeremy Irvin Pranav Rajpurkar M. Ko Yifan Yu Silviana Ciurea-Ilcus ... D. Larson C. Langlotz Bhavik Patel M. Lungren A. Ng 112 2,602 0 21 Jan 2019
Latent Space Autoregression for Novelty Detection Davide Abati Angelo Porrello Simone Calderara Rita Cucchiara 113 435 0 04 Jul 2018
Deep Anomaly Detection Using Geometric Transformations I. Golan Ran El-Yaniv 92 606 0 28 May 2018