# Attribute Restoration Framework for Anomaly Detection

arXiv: https://arxiv.org/abs/1911.10676 · HTML: https://mlsanity.com/paper/1911.10676/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 16 of 16)

- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [SSD: A Unified Framework for Self-Supervised Outlier Detection](https://mlsanity.com/paper/2103.12051/) (2021)
- [DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection](https://mlsanity.com/paper/2211.11317/) (2022)
- [Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.13118/) (2021)
- [OODformer: Out-Of-Distribution Detection Transformer](https://mlsanity.com/paper/2107.08976/) (2021)
- [Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection](https://mlsanity.com/paper/2210.14485/) (2022)
- [DOC3-Deep One Class Classification using Contradictions](https://mlsanity.com/paper/2105.07636/) (2021)
- [Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection](https://mlsanity.com/paper/2302.09794/) (2023)
- [Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework](https://mlsanity.com/paper/2102.01882/) (2021)
- [Connective Reconstruction-based Novelty Detection](https://mlsanity.com/paper/2210.13917/) (2022)
- [FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection](https://mlsanity.com/paper/2309.07068/) (2023)
- [P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly Screening](https://mlsanity.com/paper/2108.03815/) (2021)
- [OCFormer: One-Class Transformer Network for Image Classification](https://mlsanity.com/paper/2204.11449/) (2022)
- [Collaborative Anomaly Detection](https://mlsanity.com/paper/2209.09923/) (2022)
- [Tensor-to-Tensor Models with Fast Iterated Sum Features](https://mlsanity.com/paper/2506.06041/) (2025)

## Abstract (arXiv)

With the recent advances in deep neural networks, anomaly detection in multimedia has received much attention in the computer vision community. While reconstruction-based methods have recently shown great promise for anomaly detection, the information equivalence among input and supervision for reconstruction tasks can not effectively force the network to learn semantic feature embeddings. We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors. Through forcing the network to restore the original image, the semantic feature embeddings related to the erased attributes are learned by the network. During testing phases, because anomalous data are restored with the attribute learned from the normal data, the restoration error is expected to be large. Extensive experiments have demonstrated that the proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets, especially on ImageNet, increasing the AUROC of the top-performing baseline by 10.1%. We also evaluate our method on a real-world anomaly detection dataset MVTec AD and a video anomaly detection dataset ShanghaiTech.