PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

17 November 2020

Papers citing "PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization"

8 / 108 papers shown

Title
A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection Matías Tailanián Pablo Musé Álvaro Pardo 37 8 0 05 Oct 2021
Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection Marija Ivanovska Vitomir Štruc 20 2 0 28 Sep 2021
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations Oliver Rippel Arnav Chavan Chucai Lei Dorit Merhof 44 18 0 09 Aug 2021
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection Pierre Gutierrez Antoine Cordier Thais Caldeira Théophile Sautory 18 4 0 02 Jun 2021
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation Jin-Hwa Kim Do-Hyeong Kim Saehoon Yi Taehoon Lee 24 53 0 31 May 2021
Inpainting Transformer for Anomaly Detection Jonathan Pirnay K. Chai ViT 107 165 0 28 Apr 2021
Student-Teacher Feature Pyramid Matching for Anomaly Detection Guodong Wang Shumin Han Errui Ding Di Huang 23 214 0 07 Mar 2021
Deep One-Class Classification via Interpolated Gaussian Descriptor Yuanhong Chen Yu Tian Guansong Pang G. Carneiro VLM 30 96 0 25 Jan 2021