A multi-source domain adaptive migration method and system based on confrontational learning

Abstract

The present invention discloses a multi-source domain adaptive migration method and system based on confrontational learning. The method includes the following steps: step 1, using each source domain data for pre-training and initializing the representation network and classifier of the target model; step 2 , use multi-source domain data and target domain data to perform multi-way confrontation, and update the representation network and multi-way discriminator of the target model; Step 3, calculate the confrontation score between each source domain and target domain; Step 4, based on each source domain domain classifier and adversarial score to classify the target domain; Step 5, select high-confidence target domain pseudo-samples to fine-tune the representation network and classifier of the target model; Step 6, return to Step 2, and proceed to Step 2-5 until the model Stop training when it converges or reaches the maximum number of iterations, the present invention can no longer rely on the assumption that the label set of a single source domain is consistent with the target domain, and can effectively avoid the negative transfer phenomenon existing in the multi-source domain adaptation process.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a multi-source domain adaptive migration method and system based on adversarial learning. Background technique [0002] With the continuous generation of large-scale data and the difficulty of relying on manual information labeling, domain adaptation transfer methods have gradually become a very important research topic in the field of machine learning. Domain adaptation learning aims to adapt the feature distribution between data in different domains, improve the performance of classifiers after migration between different domains, and solve the problem of lack of labeling information in target domain data. The domain adaptation transfer method is also a key technical means in the industry, and has important applications in many fields such as face recognition, automatic driving, and medical imaging. [0003] At present, most domain adaptation learning methods ...

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Problems solved by technology

[0005] In order to overcome the deficiencies in the above-mentioned prior art, the purpose of the present invention is to provide a multi-source domain adaptation migration method and system based on adversarial learning, so as to extend the existing single-source domain adaptation process based on adversarial learning to multi-source domain adaptation. Source domain adaptation no longer relies on the assumption that a single source domain label set is consistent with the target domain, and can effectively avoid the negative transfer phenomenon that exists in the process of multi-source domain adaptation

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