A Self-Supervised Domain Adaptive Deep Learning Method Based on Consistency Training

Abstract

The invention discloses a self-supervised field adaptive deep learning method based on consistency training. This method first constructs a set of data augmentation transformations, and defines a label for each transformation. Construct a classification task for the source domain samples and their corresponding category labels; apply the data enhancement transformation to the source domain and target domain samples, and construct a self-supervised learning task by minimizing the error of predicting the transformation category; for the source domain and target domain Domain samples, by minimizing the KL divergence (Kullback-Leibler Divergence) of the output of the transformed samples and the original samples on the classification task, construct a consistent training task; construct a multi-task learning network, and combine the classification, automatic The supervised learning and consistency training tasks are jointly trained. This method does not need to label the samples in the target domain, can effectively learn the feature representation of the target domain, and improve the performance of sample classification and recognition in the target domain. The present application also discloses a domain-adaptive deep learning readable storage medium, which also has the above beneficial effects.

Description

technical field [0001] The invention belongs to the field of new generation information technology, and specifically relates to a field-adaptive deep learning image classification method and a readable storage medium. Background technique [0002] Machine learning, especially deep learning models usually require a large number of labeled samples for supervised learning. For example, the classification and recognition of images, texts, etc. need to collect a large number of samples, and also need to label the corresponding category of each sample. After the model is trained on the labeled data, it is applied to the test data. Supervised learning is a very effective method when the test data has the same distribution as the training data. However, in practical applications, the distribution of test data and training data is usually different, which leads to a sharp decline in the performance of the model on the test data set. [0003] Domain adaptation is a kind of technical...

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