Unsupervised domain adaptation method based on adversarial learning loss function

Abstract

The invention discloses an unsupervised domain adaptation method based on an adversarial learning loss function, and the method comprises the steps: (1), generating a high-level feature of a source domain image through a feature extraction network G, carrying out the cross entropy loss with a real label through a classifier C, generating a confusion matrix through a domain discriminator D, and correcting a pseudo label into the real label; and (2) generating high-level features of the target domain image through a feature extraction network G, generating pseudo tags through a classifier C, generating a confusion matrix of the high-level features through a domain discriminator D, and correcting the pseudo tags to be in opposite distribution. (3) confronting and optimizing the loss functionby a feature generator and a discriminator. In addition, for the confusion matrix on the target domain, a correction label is generated and serves as a label of the target domain, and the classifier is optimized. By utilizing the method and the device, the noise of the pseudo tag can be corrected in unsupervised domain adaptation, and the distribution difference between the domains is matched, sothat the classification precision of the target domain is improved.

Description

technical field [0001] The invention belongs to the field of unsupervised domain adaptation of transfer learning, and in particular relates to an unsupervised domain adaptation method based on an adversarial learning loss function. Background technique [0002] In recent years, deep learning has made impressive progress on classification tasks. The success of deep neural networks is based on large-scale datasets with a large number of labeled samples. However, in many practical situations, a large number of labeled samples are not available. Deep neural networks pre-trained on existing datasets cannot generalize well to new data with different appearance features. Essentially, the difference in data distribution between domains makes it difficult to transfer knowledge from the source domain to the target domain. This transfer problem is known as the domain transfer problem. [0003] Unsupervised domain adaptation aims to solve the domain transfer problem mentioned above,...

AI Technical Summary

Problems solved by technology

However, the alignment between the source and target feature distributions is implicit and not theoretically guaranteed

Without matching domain distribution, self-training-based methods lead to performance degradation in the case of shallow networks

Images