Badcase discovery method and system based on small sample learning
A discovery method and small sample technology, applied in the fields of instruments, character and pattern recognition, computer components, etc., can solve the problems of biased data sources, inability to find bad cases, and small random coverage, and achieve the effect of reducing time-consuming
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[0026] The present invention will be further described below in conjunction with accompanying drawing:
[0027] Such as figure 1 As shown, a badcase discovery method based on small sample learning includes the following steps:
[0028] S1: Data preprocessing, randomly obtain multiple small samples from the marked training corpus, and divide the samples into support set and target set, the small samples are in the data form of N-way K-shot, N represents each The number of semantics included in the small training batch, K represents the number of training samples under each semantic, N is generally less than 100, and K is generally less than 20. Each small training process data is divided into a support set and a target set. Generally, the same N-way K-shot form is used. After the model is trained once on the support set, the loss of the model will be obtained under the paired target set (loss function value) for backpropagation to update model parameters. The labeled trainin...
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