Data processing model training method and device, data processing method and device and electronic equipment

Abstract

The invention provides a data processing model training method, and the method comprises the steps: obtaining a base class data sample set, and extracting features of the base class data sample set through a feature extraction network; processing the extracted features of the base class data sample set through a guidance network in the data processing model so as to determine parameters of the guidance network; processing the extracted features of the base class data sample set through a parameter generation network in the data processing model to determine initial parameters of the parametergeneration network; and iteratively updating the initial parameters of the parameter generation network in the data processing model through the output result of the guidance network and the base class data sample set. The invention further provides a data processing method and device and a storage medium. According to the invention, the training precision and the training speed of the data processing model can be improved, so the data processing model can adapt to use scenes of different small sample data.

Description

Technical field [0001] The invention relates to data processing technology in a neural network model, and in particular to a data processing model training method, data processing method, device, electronic equipment and storage medium. Background technique [0002] The neural network model based on machine learning can recognize each category and realize data processing in different application scenarios. For example, in image, natural language processing, risk warning and other application scenarios, large-scale classification and recognition of large amounts of data are implemented to quickly and accurately obtain relevant classification prediction results, and accelerate the implementation of functions in the application scenario. For example, in the risk control of financial business links such as payment, lending, and wealth management, the data distribution of different customer groups varies greatly, and there are many customer groups with small sample characteristics. T...

AI Technical Summary

Problems solved by technology

Therefore, it is often difficult to collect enough labeled samples for traditional machine learning to extract risk control-related pattern features from the data, which is prone to model overfitting and introduces new noise, which affects the data processing effect of the model

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