Anomaly detection aims to find different patterns from those seen previously. It is usually regarded as a oneclassification problem where abnormal classes are often scarce or not well defined, while the target class (training objects) are sufficient. Recently, several methods have achieved excellent performance through an auxiliary multi-class task(such as rotation predict) used in self-supervised learning. However, these classification-based approaches which adopt the crossentropy loss have inherent defects in anomaly detection. Specifically, the relative measure of cross-entropy may result that a normal sample suffered from a low score would be misclassified as an abnormality. In order to solve this problem, we propose an Absolute Measurement Anomaly Detection (AMAD), to constrain the distribution of activations of each input in the classification network. In details, this technique encourages the output of the ground truth class to be higher, vice versa for unrelated classes. Furtherly, different from the previous evaluation methods that counting the log-softmax activation of the model as normality score, we ignore the log-softmax since the score would be affected heavily provided that more misclassification occurs. We present experiments both in image datasets(CIFAR-10, Fashion-MNIST) and tabular datasets(KDDCUP et al.), which show that our technique achieves better performance in terms of AUROC and F1 score when compared with previous similar methods.
Recently channel attention mechanism has been widely used to improve the performance of convolutional neural networks. However, most channel attention mechanisms applied to the backbone convolutional neural networks of the computer vision use the global pooling features of the output from each block to obtain the channel attention weights of corresponding channels, ignore the spatial information of the corresponding original features and the potential relationship between adjacent layers. For insufficient utilization of space information of origin features and inability to adaptively learn the potential association of all features in a block before the process of producing channel attention weights, we propose a new Cross-layer Channel Attention Mechanism(CCAM), in which a matrix with spatial information is used to replace the global pooling operation, uses the input and output features of each block as the inputs, and outputs the channel attention weights of corresponding features simultaneously. Compared with other attention mechanisms, the CCAM have the following three advantages: first, it makes full use of the spatial information of each layer of features; second, it encourages feature reuse and fusion; third, it is better at discovering the potential relationship between the features of different layers in a block. Our simulation results have demonstrated that CCAM can effectively extract the attention weights of diffident layers, and achieve better performance on CIFAR- 10, CIFAR-100, ImageNet-1K, MS COCO detection, and VOC detection with small additional computational cost compared with the corresponding convolutional neural network.
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