# Novelty Detection via Non-Adversarial Generative Network

arXiv: https://arxiv.org/abs/2002.00522 · HTML: https://mlsanity.com/paper/2002.00522/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods](https://mlsanity.com/paper/2111.01604/) (2021)
- [Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks](https://mlsanity.com/paper/2111.12379/) (2021)

## Abstract (arXiv)

One-class novelty detection is the process of determining if a query example differs from the training examples (the target class). Most of previous strategies attempt to learn the real characteristics of target sample by using generative adversarial networks (GANs) methods. However, the training process of GANs remains challenging, suffering from instability issues such as mode collapse and vanishing gradients. In this paper, by adopting non-adversarial generative networks, a novel decoder-encoder framework is proposed for novelty detection task, insteading of classical encoder-decoder style. Under the non-adversarial framework, both latent space and image reconstruction space are jointly optimized, leading to a more stable training process with super fast convergence and lower training losses. During inference, inspired by cycleGAN, we design a new testing scheme to conduct image reconstruction, which is the reverse way of training sequence. Experiments show that our model has the clear superiority over cutting-edge novelty detectors and achieves the state-of-the-art results on the datasets.