Combining GANs and AutoEncoders for Efficient Anomaly Detection (2011.08102v2)

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/.

• The paper presents CBiGAN, a novel model that leverages a consistency constraint with GANs and autoencoders to enhance anomaly detection.
• The methodology integrates a generator, encoder, and discriminator to improve reconstruction accuracy while reducing computational costs.
• Evaluation on the MVTec AD dataset demonstrates superior texture anomaly detection performance and competitive object detection results.

Combining GANs and AutoEncoders for Efficient Anomaly Detection

The paper "Combining GANs and AutoEncoders for Efficient Anomaly Detection" introduces CBiGAN, a novel approach to image anomaly detection that leverages both Generative Adversarial Networks (GANs) and AutoEncoders (AEs). This method incorporates a consistency constraint as a regularization term to enhance the reconstruction capabilities of Bidirectional GANs (BiGANs). Through this approach, the authors aim to address and improve the often imprecise reconstructions in standard BiGAN anomaly detectors, thereby enhancing anomaly detection performance while reducing computational costs.

Methodology Overview

The primary contribution of the paper lies in the development of CBiGAN, which integrates a consistency constraint within the encoder and decoder frameworks of BiGANs. This constraint is designed to ensure alignment between encoding and decoding processes, thereby improving the precision of reconstructions.

1. Architecture:
   • CBiGAN employs three modules: a generator, encoder, and discriminator. The encoder maps input images to a latent space, and the generator reconstructs images from this space.
   • The discriminator evaluates the authenticity of the image-latent pair, guiding the generator and encoder to project real samples accurately into the latent space while reconstructing the input image.
2. Consistency Constraint:
   • A cycle consistency loss is introduced, encouraging reconstructed images to align closely with the original samples by minimizing the pixel-based L1 reconstruction error and latent space error.
3. Anomaly Score:
   • An anomaly score is computed by combining reconstruction error and discriminator feature-based error, allowing the distinction between normal and anomalous samples.

Evaluation and Results

The authors evaluate CBiGAN on the MVTec AD dataset, a benchmark for unsupervised anomaly detection, particularly within industrial applications. Their empirical evaluation demonstrates notable improvements over baseline and state-of-the-art methods, particularly for texture-based anomaly detection:

• Texture Anomalies: CBiGAN sets a new state of the art by achieving superior accuracies, outperforming both iterative methods and single-pass approaches in balanced accuracy and area under the ROC curve (auROC).
• Object Anomalies: For object categories, CBiGAN performs competitively, although variations across specific cases suggest that tuning hyperparameters might further enhance performance.

Implications and Future Work

The implications of this research are significant, especially for applications in industrial and biomedical anomaly detection, where precision and computational efficiency are crucial. By effectively combining the modeling capabilities of GANs with the reconstruction efficiency of AEs, CBiGAN presents a promising approach for detecting anomalies without incurring the heavy computational costs typically associated with GAN-based methods.

Looking forward, further exploration of CBiGAN's applicability to other domains is warranted, potentially extending its use to anomaly localization tasks. Additionally, examining the impact of parameter tuning tailored to specific anomaly detection scenarios could yield further performance enhancements.

Overall, CBiGAN represents a valuable advancement in unsupervised anomaly detection methodologies, offering a robust, efficient solution with broader applicability across complex image datasets.