Method for generating unbiased deep learning model based on transfer learning

Abstract

The invention discloses a method for generating an unbiased deep learning model based on transfer learning. The method comprises the following steps: (1) constructing a task label with a sample imageand an original data set of a biased label; (2) training a biased deep learning model by using the original data set; (3) constructing and training an adversarial attack network, and attacking the original data set by using the trained adversarial network without prejudice; (4) training an initial unbiased deep learning model with the same structure as the biased deep learning model by utilizing the unbiased data set; and (5) preparing a third feature extractor, and forming an unbiased deep learning model by the third feature extractor with determined parameters of the third feature extractorand a second classifier included in the trained initial unbiased deep learning model based on a transfer learning strategy, so as to ensure the fairness of the deep learning model during automatic decision making according to the input image. The accuracy of image recognition is improved.

Description

technical field [0001] The invention belongs to the field of deep learning, and in particular relates to a method for generating an unbiased deep learning model based on migration learning. Background technique [0002] With its powerful ability to learn the inherent laws and highly abstract features of sample data sets, deep learning helps people make decisions automatically and solve many complex pattern recognition problems, so it is applied in medical diagnosis, speech recognition, image recognition, natural Language understanding, advertising, credit, employment, education, and criminal justice, among other areas, have worked well. With the continuous exploration and innovation of researchers, the performance of deep learning has been continuously improved, and its application has become more and more extensive, which has had a profound impact on people's daily life. [0003] Although deep learning can help people obtain more accurate predictions, the latest research s...

AI Technical Summary

Problems solved by technology

However, in the existing methods for eliminating the bias of the deep learning model, only one of the factors of the bias of the model is often considered

For example, to eliminate bias directly by preprocessing the sample data set, the problem with this approach is that the trained model has not learned the biased data set, so some biased data may be recognized when identifying the original biased data. Or irrelevant features are very sensitive, and at the same time, because the deep learning model will amplify the influence of this bias, the trained model can only eliminate part of the bias

Images