Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection
This work addresses anomaly detection for applications requiring reliable identification of outliers, offering a method with stronger theoretical guarantees and practical gains over existing approaches.
The paper tackles the problem of anomaly detection by proposing a Regularized Cycle Consistent Generative Adversarial Network (RCGAN) that improves detection accuracy through adversarial training and a novel loss function, achieving significant improvements on benchmarks like KDDCUP, Arrhythmia, Thyroid, Musk, and CIFAR10 datasets.
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.