Anomaly localization is an important problem in computer vision which
involves localizing anomalous regions within images with applications in
industrial inspection, surveillance, and medical imaging. This task is
challenging due to the small sample size and pixel coverage of the anomaly in
real-world scenarios. Most prior works need to use anomalous training images to
compute a class-specific threshold to localize anomalies. Without the need of
anomalous training images, we propose Convolutional Adversarial Variational
autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a
convolutional latent variable to preserve the spatial information. In the
unsupervised setting, we propose an attention expansion loss where we encourage
CAVGA to focus on all normal regions in the image. Furthermore, in the
weakly-supervised setting we propose a complementary guided attention loss,
where we encourage the attention map to focus on all normal regions while
minimizing the attention map corresponding to anomalous regions in the image.
CAVGA outperforms the state-of-the-art (SOTA) anomaly localization methods on
MVTec Anomaly Detection (MVTAD), modified ShanghaiTech Campus (mSTC) and
Large-scale Attention based Glaucoma (LAG) datasets in the unsupervised setting
and when using only 2% anomalous images in the weakly-supervised setting. CAVGA
also outperforms SOTA anomaly detection methods on the MNIST, CIFAR-10,
Fashion-MNIST, MVTAD, mSTC and LAG datasets.

使用卷积对抗变分自编码器与引导注意力和注意力扩张损失的方法，可在无监督和弱监督低异常样本下精确定位异常区域，结果表明 CAVGA 在几个测试数据集上优于目前最先进的异常检测方法。