The DOI matches its Crossref record; 35 of the 40 references it cites were found in a registry or an index.
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
Open accessAbstract
Despite inherent ill-definition, anomaly detection is a research endeavour of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly detection is considered as the detection of outliers within a given data distribution based on some measure of normality. The most significant challenge in real-world anomaly detection problems is that available data is highly imbalanced towards normality (i.e. non-anomalous) and contains at most a sub-set of all possible anomalous samples - hence limiting the use of well-established supervised learning methods. By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain, and hence detect abnormality based on deviation from this model. Our proposed approach employs an encoder-decoder convolutional neural network with skip connections to thoroughly capture the multi-scale distribution of the normal data distribution in image space. Furthermore, utilizing an adversarial training scheme for this chosen architecture provides superior reconstruction both within image space and a lower-dimensional embedding vector space encoding. Minimizing the reconstruction error metric within both the image and hidden vector spaces during training aids the model to learn the distribution of normality as required. Higher reconstruction metrics during subsequent test and deployment are thus indicative of a deviation from this normal distribution, hence indicative of an anomaly. Experimentation over established anomaly detection benchmarks and challenging real-world datasets, within the context of X-ray security screening, shows the unique promise of such a proposed approach.
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“Self-supervised learning with only positive pairs [14], [16] has resulted in competitive performance in comparison to contrastive learning which requires both negative pairs and positive pairs [13], [17].”
From Self-Supervised Learning for Anomaly Detection With Dynamic Local Augmentation (2021) · cites this paper as [13]
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
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“The training objective L from Equation 4 is optimized via Adam [34] optimizer with an initial learning rate lr = 2e−3 with a lambda decay, and momentums β1 = 0.”
MethodInfluentialBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · cited 1×
“An innovative and seminal work of Radford and Chintala [23] pioneered a new approach to stabilize GAN training by using fullyconvolutional layers and batch normalization [24] throughout the network.”
BackgroundGAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · cited 3×
“The network architecture of the discriminator D follows the same structure as the discriminator of the DCGAN approach presented in [23].”
BackgroundChandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
(2017) · Cited by 11,625
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863 · cited 1×
“Training AAE provides superior reconstruction as well as the capability of controlling the latent space [20], [27], [28].”
BackgroundImproved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965 · cited 1×
“Recently, there has been a surge of interest in addressing the instability issues via several empirical methodologies [21], [22].”
Background
Research works printed but not linked: 3
- 10.3390/jimaging4020036 · DOI
- goodfellow, 2014, Generative adversarial nets, Advances in neural information processing systems, 2672
- 10.1023/B:AIRE.0000045502.10941.a9 · DOI · Paper page
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Sorted by citations
Items 101–107 of 107
Mtddpm: Visual Anomaly Detection with Multiscale Denoising Diffusion Probabilistic ModelPreprint
Wang, X., & Li, W. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4654306
A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection
Zhang, L., Kan, S., Cen, Y., Chen, X., Zhang, L., & Huang, Y. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.046924
Discrepancy Constraint Learning for Industrial Unsupervised Anomaly DetectionPreprint
Song, Y., Liu, Z., Liu, H., Tang, R., Duan, G., & Tan, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4786002
A Simple Model for Fault Detection of UAV Photoelectric Load Based on Image Classification
Xue, S., Shen, Y., Li, C., & Li, J. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2735/1/012010
“Akcay et al. added a skip connection between the encoder and generator to make the positioning of abnormal parts more accurate [7].”
BackgroundAsthana, S., & Goel, A. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10726188
“Skip-GANomaly [6] represents a noteworthy extension and improvement upon the GANomaly model.”
BackgroundMethodInfluentialTowards consistent representation for reverse distillation on lung CT-scan images
Li, Z., Xu, S., Li, W., Xu, Q., Cai, W., & Wu, X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107767
Exploring the Relationship Between Samples and Masks for Robust Defect Localization
Lin, J., Xue, H., Sun, F., & Yan, Y. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i11.33324
“Despite attempts [1, 2, 9, 27] to address these issues, they share a similar paradigm: identifying defects by modeling the normal distribution.”
Background
Metadata
- Authors
- Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P.
- Affiliations
- Durham University (GB)
- ORCID
- Breckon, Toby P.
- Venue
- 2019 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2019-07
- Pages
- 1-8
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2911672077
- Cited by
- 103
- References
- 40
- Citations by year
- 2025: 13 · 2024: 19 · 2023: 19 · 2022: 14 · 2021: 25 · 2020: 8 · 2019: 5
- FWCI
- 4.97
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- anomaly detection, unsupervised anomaly detection, encoder-decoder architecture, skip connections, adversarial training, class imbalance, reconstruction error, X-ray security screening, image reconstruction
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