Twin classifier certainty maximization method for cross-domain complex vision task
A classifier and deterministic technology, applied in the field of transfer learning, can solve the problems of single adaptation scene and insufficient discriminability, and achieve the effect of improving model performance, ensuring discriminability and predicting diversity.
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[0040] In order to make the purpose, technical solutions and advantages of the present invention clearer, specific examples of the method of the present invention will be further described in detail.
[0041] For ease of understanding, in this example, include a source domain with the label where: n s is the sample size, is the i-th sample in the source domain, is the corresponding label, and an unlabeled target domain where: n t is the number of samples in the target domain, is the i-th sample in the target domain; the goal of the method of the present invention is to migrate the deep neural network model trained on the source domain samples to the target domain, and enable it to learn a good transferable Feature representation of sex and discriminability, so as to achieve good performance of the model on the target domain, that is, φ:X t →Y t ; The model framework of the method of the present invention comprises a feature generator G and two twin classifiers C ...
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