Weimin Shangguan
ABSTRACT
In this work, we propose a novel semi-supervised anomaly detection approach based on deep generative models with Transformers for identifying unusual (abnormal) images from normal ones. Our approach is based on the combination of autoencoder (AE) and generative adversarial networks (GAN). Similar to the vanilla GAN, our model is mainly composed of the generator and discriminator. The generator adopts an encoder-decoderencoder structure to extract meaningful latent representations, in which each encoder is constructed by a Transformer whereas the decoder is realized through the transposed convolution. The discriminator, which is built upon another Transformer, is used to distinguish whether the given image comes from the generator or the training set, while optimizing the encoder in the generator for better latent representations through adversarial training. The distribution of the normal data can be learned by minimizing the gap between the original image space and the latent image space during the training process. The abnormal images are detected if their distributions are different from the learned normal distributions. The merits of the proposed anomaly detection approach are demonstrated by comparing it with other generative anomaly detection approaches through experiments on three benchmark image data sets.
- Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.Google Scholar
Digital Library
- Taboada-Crispi, A., Sahli, H., Hernandez-Pacheco, D., & Falcon-Ruiz, A. (2009). Anomaly detection in medical image analysis. In Handbook of research on advanced techniques in diagnostic imaging and biomedical applications (pp. 426-446). IGI Global.Google ScholarCross Ref
- Ahmed, M., Mahmood, A. N., & Islam, M. R. (2016). A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, 278-288..Google ScholarDigital Library
- Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.Google ScholarDigital Library
- Perera, P., Nallapati, R., & Xiang, B. (2019). Ocgan: One-class novelty detection using gans with constrained latent representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2898-2906).Google ScholarCross Ref
- Markou, M., & Singh, S. (2003). Novelty detection: a review—part 1: statistical approaches. Signal processing, 83(12), 2481-2497.Google Scholar
- Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019). Latent space autoregression for novelty detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 481-490).Google ScholarCross Ref
- Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., ... & Kloft, M. (2018, July). Deep one-class classification. In International conference on machine learning (pp. 4393-4402). PMLR.Google Scholar
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.Google Scholar
- Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017, June). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging (pp. 146-157). Springer, Cham.Google ScholarCross Ref
- Zenati, H., Foo, C. S., Lecouat, B., Manek, G., & Chandrasekhar, V. R. (2018). Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222.Google Scholar
- Donahue, J., Krähenbühl, P., & Darrell, T. (2016). Adversarial feature learning. arXiv preprint arXiv:1605.09782.Google Scholar
- Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2018, December). Ganomaly: Semi-supervised anomaly detection via adversarial training. In Asian conference on computer vision (pp. 622-637). Springer, Cham.Google Scholar
- Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018). Adversarially learned one-class classifier for novelty detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3379-3388).Google ScholarCross Ref
- Azulay, A., & Weiss, Y. (2018). Why do deep convolutional networks generalize so poorly to small image transformations?. arXiv preprint arXiv:1805.12177.Google Scholar
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).Google Scholar
- Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.Google Scholar
- Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., & Wang, Y. (2021). Transformer in transformer. arXiv preprint arXiv:2103.00112.Google Scholar
- Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., & Sutskever, I. (2020, November). Generative pretraining from pixels. In International Conference on Machine Learning (pp. 1691-1703). PMLR.Google Scholar
- Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020, August). End-to-end object detection with transformers. In European Conference on Computer Vision (pp. 213-229). Springer, Cham.Google Scholar
- Jiang, Y., Chang, S., & Wang, Z. (2021). Transgan: Two transformers can make one strong gan. arXiv preprint arXiv:2102.07074, 1(3).Google Scholar
- Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2019, May). Self-attention generative adversarial networks. In International conference on machine learning (pp. 7354-7363). PMLR.Google Scholar
- Hojjati, H., & Armanfard, N. (2021). DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection. arXiv preprint arXiv:2106.05410.Google Scholar
- Schölkopf, B., Williamson, R. C., Smola, A. J., Shawe-Taylor, J., & Platt, J. C. (1999, December). Support vector method for novelty detection. In NIPS (Vol. 12, pp. 582-588).Google Scholar
- Parzen, E. (1962). On estimation of a probability density function and mode. The annals of mathematical statistics, 33(3), 1065-1076.Google Scholar
- Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. Advances in neural information processing systems, 29, 2234-2242.Google Scholar
- Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016). Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2536-2544).Google ScholarCross Ref
- Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., & Chen, H. (2018, February). Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International conference on learning representations.Google Scholar
- Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.Google Scholar
- Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008, December). Isolation forest. In 2008 eighth ieee international conference on data mining (pp. 413-422). IEEE.Google Scholar
- Zhou, C., & Paffenroth, R. C. (2017, August). Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 665-674).Google ScholarDigital Library
- An, J., & Cho, S. (2015). Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, 2(1), 1-18.Google Scholar
- Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017, June). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging (pp. 146-157). Springer, ChamGoogle ScholarCross Ref
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