Visual Anomaly Detection for Images: A Survey

2021/09/27 by Jie Yang, Ruijie Xu, Yang, Jie +5 · 1 citation
Computer Science · Medicine · #Anomaly Detection Techniques and Applications #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2109.13157

openalex publication_date 2021/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. This problem has attracted a considerable amount of attention in relevant research communities. Especially in recent years, the development of deep learning has sparked an increasing interest in the visual anomaly detection problem and brought a great variety of novel methods. In this paper, we provide a comprehensive survey of the classical and deep learning-based approaches for visual anomaly detection in the literature. We group the relevant approaches in view of their underlying principles and discuss their assumptions, advantages, and disadvantages carefully. We aim to help the researchers to understand the common principles of visual anomaly detection approaches and identify promising research directions in this field.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ImageNet Large Scale Visual Recognition Challenge
• Estimating the Support of a High-Dimensional Distribution
• Adversarial Feature Learning
• Deep Anomaly Detection Using Geometric Transformations
• Do Deep Generative Models Know What They Don't Know?
• OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations
• Holistically-Nested Edge Detection
• Unsupervised Anomaly Detection with Generative Adversarial Networks to\n Guide Marker Discovery
• Learning to Incorporate Structure Knowledge for Image Inpainting
• Sub-Image Anomaly Detection with Deep Pyramid Correspondences
• Self-Supervised Representation Learning for Visual Anomaly Detection
• Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
• CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Cited by

• Latent Anomaly Detection: Masked VQ-GAN for Unsupervised Segmentation in Medical CBCT

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