# Novelty Detection with GAN

arXiv: https://arxiv.org/abs/1802.10560 · HTML: https://mlsanity.com/paper/1802.10560/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

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

- [Towards creativity characterization of generative models via group-based subset scanning](https://mlsanity.com/paper/2104.00479/) (2021)
- [Enhanced Isotropy Maximization Loss: Seamless and High-Performance Out-of-Distribution Detection Simply Replacing the SoftMax Loss](https://mlsanity.com/paper/2105.14399/) (2021)
- [Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines](https://mlsanity.com/paper/2105.04708/) (2021)
- [Unsupervised Class-Incremental Learning Through Confusion](https://mlsanity.com/paper/2104.04450/) (2021)
- [Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning](https://mlsanity.com/paper/2201.09717/) (2022)
- [On the link between generative semi-supervised learning and generative open-set recognition](https://mlsanity.com/paper/2303.11702/) (2023)
- [Towards Creativity Characterization of Generative Models via Group-based Subset Scanning](https://mlsanity.com/paper/2203.00523/) (2022)
- [KL Regularized Normalization Framework for Low Resource Tasks](https://mlsanity.com/paper/2212.11275/) (2022)

## Abstract (arXiv)

The ability of a classifier to recognize unknown inputs is important for many classification-based systems. We discuss the problem of simultaneous classification and novelty detection, i.e. determining whether an input is from the known set of classes and from which specific class, or from an unknown domain and does not belong to any of the known classes. We propose a method based on the Generative Adversarial Networks (GAN) framework. We show that a multi-class discriminator trained with a generator that generates samples from a mixture of nominal and novel data distributions is the optimal novelty detector. We approximate that generator with a mixture generator trained with the Feature Matching loss and empirically show that the proposed method outperforms conventional methods for novelty detection. Our findings demonstrate a simple, yet powerful new application of the GAN framework for the task of novelty detection.