Multi-source domain adaptive model and method based on partial feature alignment

Abstract

The invention discloses a multi-source domain adaptive model and method based on partial feature alignment, and the method comprises the steps: enabling a feature selection module for partial featureextraction to generate a selection vector of a feature level according to the similarity of each feature dimension of a source domain and a target domain on the basis of a conventional convolutional neural network or residual neural network feature extractor, wherein after the selection vector acts on the initial feature map, part of features highly related to the target domain in the source domain can be screened out. On the basis, the invention further provides three partial feature alignment loss functions for intra-class, inter-domain and inter-class features, so that the purified featuremap has better distinguishability for a classifier, and partial features related to a source domain and a target domain are highlighted. The method is used for the multi-source field self-adaptive classification data set, and compared with an existing multi-source field self-adaptive model, the classification accuracy is higher, and the feature selection effect is better.

Description

technical field [0001] The invention belongs to the multi-source domain self-adaptive branch in computer vision and migration learning, and specifically relates to a multi-source domain self-adaptive model structure that aligns partial features through high-order moment matching, and designs an alignment loss function on the feature map to To achieve the purpose of distinguishing features from classifiers. Background technique [0002] In machine learning, supervised and semi-supervised learning using deep neural networks has achieved remarkable results. Relying on many public data sets, supervised and semi-supervised learning can be used in various tasks such as image classification, face recognition, and semantic analysis. Has a wide range of applications. However, the collection of data labels in the real world is very difficult and often requires a lot of manpower to complete. Due to the existence of inter-domain offset, the models trained on other data sets cannot be d...

AI Technical Summary

Problems solved by technology

For example, when performing feature alignment, some existing methods use all image features obtained through feature extractors, which not only include features related to the source domain and target domain, but also include The domain-specific features that appear in the source domain, if these features also participate in the feature alignment, will cause a decline in the alignment effect

Images