SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection

SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection

26 November 2021

Weidong (Tom) Cai

Zongwei Zhou

Papers citing "SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection"

5 / 5 papers shown

Title
Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt Bin-Bin Gao 14 0 0 14 May 2025
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection Yun Peng Xiao Lin Nachuan Ma Jiayuan Du Chuangwei Liu Chengju Liu Qi Chen 37 3 0 17 Feb 2025
Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image Zerui Zhang Zhichao Sun Zelong Liu Bo Du Rui Yu Zhou Zhao Yongchao Xu GAN MedIm 28 3 0 21 May 2024
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction Changhee Han L. Rundo K. Murao T. Noguchi Yuki Shimahara Z. '. Milacski S. Koshino Evis Sala Hideki Nakayama Shinichi Satoh MedIm 86 159 0 24 Jul 2020
Image Inpainting for Irregular Holes Using Partial Convolutions Guilin Liu F. Reda Kevin J. Shih Ting-Chun Wang Andrew Tao Bryan Catanzaro 142 1,912 0 20 Apr 2018