# Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer

arXiv: https://arxiv.org/abs/2211.12311 · HTML: https://mlsanity.com/paper/2211.12311/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [Inpainting Transformer for Anomaly Detection](https://mlsanity.com/paper/2104.13897/) (2021)
- [AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder](https://mlsanity.com/paper/2203.10808/) (2022)
- [A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing](https://mlsanity.com/paper/2206.10830/) (2022)
- [Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector](https://mlsanity.com/paper/2211.00349/) (2022)

## Built on by (most cited first, 4 of 4)

- [Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2312.07495/) (2023)
- [Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection](https://mlsanity.com/paper/2303.05768/) (2023)
- [Foundation Models and Transformers for Anomaly Detection: A Survey](https://mlsanity.com/paper/2507.15905/) (2025)
- [AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines](https://mlsanity.com/paper/2408.02181/) (2024)

## Abstract (arXiv)

Industrial vision anomaly detection plays a critical role in the advanced intelligent manufacturing process, while some limitations still need to be addressed under such a context. First, existing reconstruction-based methods struggle with the identity mapping of trivial shortcuts where the reconstruction error gap is legible between the normal and abnormal samples, leading to inferior detection capabilities. Then, the previous studies mainly concentrated on the convolutional neural network (CNN) models that capture the local semantics of objects and neglect the global context, also resulting in inferior performance. Moreover, existing studies follow the individual learning fashion where the detection models are only capable of one category of the product while the generalizable detection for multiple categories has not been explored. To tackle the above limitations, we proposed a self-induction vision Transformer(SIVT) for unsupervised generalizable multi-category industrial visual anomaly detection and localization. The proposed SIVT first extracts discriminatory features from pre-trained CNN as property descriptors. Then, the self-induction vision Transformer is proposed to reconstruct the extracted features in a self-supervisory fashion, where the auxiliary induction tokens are additionally introduced to induct the semantics of the original signal. Finally, the abnormal properties can be detected using the semantic feature residual difference. We experimented with the SIVT on existing Mvtec AD benchmarks, the results reveal that the proposed method can advance state-of-the-art detection performance with an improvement of 2.8-6.3 in AUROC, and 3.3-7.6 in AP.