# SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection

arXiv: https://arxiv.org/abs/2408.03143 · HTML: https://mlsanity.com/paper/2408.03143/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [SimpleNet: A Simple Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2303.15140/) (2023)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies](https://mlsanity.com/paper/2303.14535/) (2023)
- [DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection](https://mlsanity.com/paper/2211.11317/) (2022)
- [Segmentation-Based Deep-Learning Approach for Surface-Defect Detection](https://mlsanity.com/paper/1903.08536/) (2019)
- [Explainable Deep One-Class Classification](https://mlsanity.com/paper/2007.01760/) (2020)
- [Asymmetric Student-Teacher Networks for Industrial Anomaly Detection](https://mlsanity.com/paper/2210.07829/) (2022)
- [Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2203.14506/) (2022)
- [Mixed supervision for surface-defect detection: from weakly to fully supervised learning](https://mlsanity.com/paper/2104.06064/) (2021)
- [Inpainting Transformer for Anomaly Detection](https://mlsanity.com/paper/2104.13897/) (2021)
- [DSR -- A dual subspace re-projection network for surface anomaly detection](https://mlsanity.com/paper/2208.01521/) (2022)
- [Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection](https://mlsanity.com/paper/2207.01463/) (2022)
- [Prototypical Residual Networks for Anomaly Detection and Localization](https://mlsanity.com/paper/2212.02031/) (2022)
- [TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection](https://mlsanity.com/paper/2311.09999/) (2023)

## Built on by (most cited first, 12 of 12)

- [A Survey on Industrial Anomaly Synthesis](https://mlsanity.com/paper/2502.16412/) (2025)
- [Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection](https://mlsanity.com/paper/2505.17551/) (2025)
- [INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning](https://mlsanity.com/paper/2506.03660/) (2025)
- [Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices](https://mlsanity.com/paper/2507.03367/) (2025)
- [MoViAD: A Modular Library for Visual Anomaly Detection](https://mlsanity.com/paper/2507.12049/) (2025)
- [Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations](https://mlsanity.com/paper/2602.19881/) (2026)
- [VAD4Space: Visual Anomaly Detection for Planetary Surface Imagery](https://mlsanity.com/paper/2603.13993/) (2026)
- [AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving](https://mlsanity.com/paper/2604.15291/) (2026)
- [Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detection](https://mlsanity.com/paper/2606.01962/) (2026)
- [LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing](https://mlsanity.com/paper/2607.01949/) (2026)
- [From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection](https://mlsanity.com/paper/2608.07770/) (2026)
- [CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation](https://mlsanity.com/paper/2608.18614/) (2026)

## Abstract (arXiv)

The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that's increasingly demanded across various industries. Current approaches frequently fail to fulfil the extensive demands of these industries, which encompass high performance, consistency, and fast operation, along with the capacity to leverage the entirety of the available training data. Addressing these gaps, we introduce SuperSimpleNet, an innovative discriminative model that evolved from SimpleNet. This advanced model significantly enhances its predecessor's training consistency, inference time, as well as detection performance. SuperSimpleNet operates in an unsupervised manner using only normal training images but also benefits from labelled abnormal training images when they are available. SuperSimpleNet achieves state-of-the-art results in both the supervised and the unsupervised settings, as demonstrated by experiments across four challenging benchmark datasets. Code: https://github.com/blaz-r/SuperSimpleNet .