Due to the excessive cost of data collection as well as annotation in dedicated domains, artificial intelligence model training is difficult to achieve optimality with insufficient data. To optimize this issue, a text generation data augmentation method based on the BERT model is proposed in this paper for augmenting the small amount of available annotated data. The optimization of this data augmentation method is demonstrated by experiments. In a text classification experiment, this data augmentation method can improve the training effect of the source data by 2.9%.
Aiming at the problems of high target similarity and strong camouflage in the domain-specific, and the traditional image classification technology is difficult to achieve accurate classification, it is proposed to use the deep neural network Resnet50 as the feature extraction network, and combine the attention mechanism and improve it, which can improve the ability of learning effective features; and use depthwise separable convolution to replace standard convolution, which can reduce computational parameters and save computational space. It is verified by experiments that the accuracy of the improved algorithm in this paper is 0.71% higher than that of the Resnet50 prototype, and 0.39 % higher than that of the Resnet50 +SE algorithm model in the image classification of the domain-specific.
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