Visual Anomaly Detection for Images: A Survey

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.

References in corpus (6)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
• Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
• Spatial PixelCNN: Generating Images from Patches
• Learning to Incorporate Structure Knowledge for Image Inpainting
• Self-Supervised Representation Learning for Visual Anomaly Detection