# Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection

arXiv: https://arxiv.org/abs/2001.06591 · HTML: https://mlsanity.com/paper/2001.06591/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol](https://mlsanity.com/paper/2106.16020/) (2021)
- [Targeted collapse regularized autoencoder for anomaly detection: black hole at the center](https://mlsanity.com/paper/2306.12627/) (2023)
- [Coincident Learning for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2301.11368/) (2023)
- [Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN](https://mlsanity.com/paper/2304.07769/) (2023)
- [Domain Translation of a Soft Robotic Arm using Conditional Cycle Generative Adversarial Network](https://mlsanity.com/paper/2508.14100/) (2025)
- [When Does Unsupervised Learning Succeed or Fail? A PoS Perspective on Reconstruction-Based Anomaly Detection](https://mlsanity.com/paper/2609.28832/) (2026)

## Abstract (arXiv)

In this paper, we investigate algorithms for anomaly detection. Previous anomaly detection methods focus on modeling the distribution of non-anomalous data provided during training. However, this does not necessarily ensure the correct detection of anomalous data. We propose a new Regularized Cycle Consistent Generative Adversarial Network (RCGAN) in which deep neural networks are adversarially trained to better recognize anomalous samples. This approach is based on leveraging a penalty distribution with a new definition of the loss function and novel use of discriminator networks. It is based on a solid mathematical foundation, and proofs show that our approach has stronger guarantees for detecting anomalous examples compared to the current state-of-the-art. Experimental results on both real-world and synthetic data show that our model leads to significant and consistent improvements on previous anomaly detection benchmarks. Notably, RCGAN improves on the state-of-the-art on the KDDCUP, Arrhythmia, Thyroid, Musk and CIFAR10 datasets.