Generative Adversarial Networks (GANs) have been used recently for anomaly detection from images, where the anomaly scores are obtained by comparing the global difference between the input and generated image. However, the anomalies often appear in local areas of an image scene, and ignoring such information can lead to unreliable detection of anomalies. In this paper, we propose an efficient anomaly detection network Skip-Attention GAN (SAGAN), which adds attention modules to capture local information to improve the accuracy of latent representation of images, and uses depthwise separable convolutions to reduce the number of parameters in the model. We evaluate the proposed method on the CIFAR-10 dataset and the LBOT dataset (built by ourselves), and show that the performance of our method in terms of area under curve (AUC) on both datasets is improved by more than 10% on average, as compared with three recent baseline methods.

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Anomaly detection is the task of detecting outliers from normal data. Numerous methods have been proposed to address this problem, including recent methods based on generative adversarial network (GAN). However, these methods are limited in capturing the long-range information in data due to the limited receptive field obtained by the convolution operation. The long-range information is crucial for producing distinctive representation for normal data belonging to different classes, while the local information is important for distinguishing normal data from abnormal data, if they belong to the same class. In this paper, we propose a novel Transformer-based architecture for anomaly detection which has advantages in extracting features with global information representing different classes as well as the local details useful for capturing anomalies. In our design, we introduce self-attention mechanism into the generator of GAN to extract global semantic information, and also modify the skip-connection to capture local details in multi-scale from input data. The experiments on CIFAR10 and STL10 show that our method provides better performance on representing different classes as compared with the state-of-the-art CNN-based GAN methods. Experiments performed on MVTecAD and LBOT datasets show that the proposed method offers state-of-the-art results, outperforming the baseline method SAGAN by over 3% in terms of the AUC metric.

Face super-resolution (FSR) is dedicated to the restoration of high-resolution (HR) face images from their low-resolution (LR) counterparts. Many deep FSR methods exploit facial prior knowledge (e.g., facial landmark and parsing map) related to facial structure information to generate HR face images. However, directly training a facial prior estimation network with deep FSR model requires manually labeled data, and is often computationally expensive. In addition, inaccurate facial priors may degrade super-resolution performance. In this paper, we propose a residual FSR method with spatial attention mechanism guided by multiscale receptive-field features (MRF) for converting LR face images (i.e., 16 × 16) to HR face images (i.e., 128 × 128). With our spatial attention mechanism, we can recover local details in face images without explicitly learning the prior knowledge. Quantitative and qualitative experiments show that our method outperforms state-of-the-art FSR methods.