Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionGenerative Adversarial NetworkSimilar Papers 제목 키워드 기반
Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN
This study presents an adversarial method for anomaly detection in real-world applications, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Previou…
Anomaly DetectionOdd One OutSemi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-cycled Cycle-GAN architecture is widely employed to achieve promising performance on face SR, but prone to produce artifacts u…
Image RestorationSuper-ResolutionIndustrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks
In this study, a new Anomaly Detection (AD) approach for industrial and medical images is proposed. This method leverages the theoretical strengths of unsupervised learning and the data availability of both normal and ab…
Anomaly DetectionGraph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection
Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. Autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to mak…
Anomaly DetectionClusteringDimensionality ReductionUnsupervised Anomaly DetectionDual-encoder Bidirectional Generative Adversarial Networks for Anomaly Detection
Generative adversarial networks (GANs) have shown promise for various problems including anomaly detection. When anomaly detection is performed using GAN models that learn only the features of normal data samples, data t…
Anomaly Detection