Combining GANs and AutoEncoders for Efficient Anomaly Detection

• URL: http://arxiv.org/abs/2011.08102v2
• Date: Thu, 26 Nov 2020 16:09:50 GMT
• Title: Combining GANs and AutoEncoders for Efficient Anomaly Detection
• Authors: Fabio Carrara (1), Giuseppe Amato (1), Luca Brombin, Fabrizio Falchi (1), Claudio Gennaro (1) ((1) ISTI CNR, Pisa, Italy)
• Abstract summary: CBiGAN is a novel method for anomaly detection in images. Our model exhibits fairly good modeling power and reconstruction consistency capability. Experiments show that the proposed method improves the performance of BiGAN formulations by a large margin.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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