# on the effectiveness of generative adversarial network on anomaly detection

arXiv: https://arxiv.org/abs/2112.15541 · HTML: https://mlsanity.com/paper/2112.15541/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 31 Dec 2021.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [Are GANs Created Equal? A Large-Scale Study](https://mlsanity.com/paper/1711.10337/) (2017)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [Adversarially Learned Inference](https://mlsanity.com/paper/1606.00704/) (2016)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Learning What and Where to Draw](https://mlsanity.com/paper/1610.02454/) (2016)
- [TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network](https://mlsanity.com/paper/1703.06412/) (2017)
- [Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations](https://mlsanity.com/paper/1802.00187/) (2018)
- [Dual Adversarial Inference for Text-to-Image Synthesis](https://mlsanity.com/paper/1908.05324/) (2019)

## Built on by (most cited first, 1 of 1)

- [Transparent Anomaly Detection via Concept-based Explanations](https://mlsanity.com/paper/2310.10702/) (2023)

## Abstract (arXiv)

Identifying anomalies refers to detecting samples that do not resemble the training data distribution. Many generative models have been used to find anomalies, and among them, generative adversarial network (GAN)-based approaches are currently very popular. GANs mainly rely on the rich contextual information of these models to identify the actual training distribution. Following this analogy, we suggested a new unsupervised model based on GANs --a combination of an autoencoder and a GAN. Further, a new scoring function was introduced to target anomalies where a linear combination of the internal representation of the discriminator and the generator's visual representation, plus the encoded representation of the autoencoder, come together to define the proposed anomaly score. The model was further evaluated on benchmark datasets such as SVHN, CIFAR10, and MNIST, as well as a public medical dataset of leukemia images. In all the experiments, our model outperformed its existing counterparts while slightly improving the inference time.