Combining GANs and AutoEncoders for Efficient Anomaly Detection

Autor:	Carrara, Fabio, Amato, Giuseppe, Brombin, Luca, Falchi, Fabrizio, Gennaro, Claudio
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this work, we propose CBiGAN -- a novel method for anomaly detection in images, where a consistency constraint is introduced as a regularization term in both the encoder and decoder of a BiGAN. Our model exhibits fairly good modeling power and reconstruction consistency capability. We evaluate the proposed method on MVTec AD -- a real-world benchmark for unsupervised anomaly detection on high-resolution images -- and compare against standard baselines and state-of-the-art approaches. Experiments show that the proposed method improves the performance of BiGAN formulations by a large margin and performs comparably to expensive state-of-the-art iterative methods while reducing the computational cost. We also observe that our model is particularly effective in texture-type anomaly detection, as it sets a new state of the art in this category. Our code is available at https://github.com/fabiocarrara/cbigan-ad/. Comment: 8 pages, 5 figures, 3 tables, pre-print, to be published in the proceedings of the 25th International Conference on Pattern Recognition (ICPR2020)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.08102 Zobrazit plný text záznamu View this record from Arxiv