Abnormal image detection method combining attention mechanism and information entropy minimization
A technology of abnormal images and detection methods, applied in neural learning methods, computer components, instruments, etc., can solve the problem that abnormal samples do not have good discrimination ability, and achieve good generalization ability, less information redundancy, and strong generalization The effect of the ability
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[0031] In order to make the present invention easier to understand and its advantages clearer, the technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the drawings and specific embodiments.
[0032] (1) In order to verify the accuracy of the present invention on abnormal image detection tasks, it is now verified by three commonly used data sets: COIL100, MNIST, and CIFAR10. The following explains the content and image size of each dataset:
[0033] COIL-100: This data set is a collection of natural pictures, including shooting 100 objects from different angles, and taking an image every 5 degrees. Each object contains 72 images, and the image size is 128*128.
[0034] MNIST: This data set is a classic data set, which is often used as an anomaly detection data set. It is a database composed of 10 categories of handwritten digits, with 6000 pictures in each category, and the size of each image is 28*28.
[0035] ...
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