# Combining GANs and AutoEncoders for Efficient Anomaly Detection

arXiv: https://arxiv.org/abs/2011.08102 · HTML: https://mlsanity.com/paper/2011.08102/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network](https://mlsanity.com/paper/2311.14897/) (2023)
- [Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors](https://mlsanity.com/paper/2202.11660/) (2022)
- [StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder](https://mlsanity.com/paper/2201.13271/) (2022)
- [DOC3-Deep One Class Classification using Contradictions](https://mlsanity.com/paper/2105.07636/) (2021)
- [Improving Novelty Detection using the Reconstructions of Nearest Neighbours](https://mlsanity.com/paper/2111.06150/) (2021)
- [P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly Screening](https://mlsanity.com/paper/2108.03815/) (2021)
- [Learning Run-time Safety Monitors for Machine Learning Components](https://mlsanity.com/paper/2406.16220/) (2024)
- [M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection](https://mlsanity.com/paper/2607.13499/) (2026)

## Abstract (arXiv)

In this work, we propose CBiGAN -- a novel method for anomaly detection in images, where a consistency constraint is introduced as a regularization term in both the encoder and decoder of a BiGAN. Our model exhibits fairly good modeling power and reconstruction consistency capability. We evaluate the proposed method on MVTec AD -- a real-world benchmark for unsupervised anomaly detection on high-resolution images -- and compare against standard baselines and state-of-the-art approaches. Experiments show that the proposed method improves the performance of BiGAN formulations by a large margin and performs comparably to expensive state-of-the-art iterative methods while reducing the computational cost. We also observe that our model is particularly effective in texture-type anomaly detection, as it sets a new state of the art in this category. Our code is available at https://github.com/fabiocarrara/cbigan-ad/.