Transfer learning algorithm based on active learning

Abstract

The invention discloses a transfer learning algorithm based on active learning, and belongs to the field of machine learning. For a general unsupervised transfer learning algorithm, a large number ofresearches exist at present, but on this basis, the improvement of the algorithm performance in a target field can be obtained at a relatively low sample labeling cost. The active transfer learning algorithm accesses a batch of data based on an active sampling method to finely tune and update network parameters after an unsupervised domain self-adaption process is carried out, so that the extracted features have good migration capability and good discrimination capability. In the invention, an active sampling strategy is not only based on a traditional information entropy method, but also provides an evaluation index of one characteristic under a transfer learning background.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a migration learning algorithm based on active query. Background technique [0002] In recent years, deep learning has achieved great success, such as computer vision, speech recognition, natural language processing, etc. State-of-the-art results have been achieved on several standard datasets. However, behind the high accuracy rate is a large amount of high-quality labeling data. The reality is that the cost of data labeling is extremely expensive, which is unaffordable for some small companies and even some large companies. And the real intelligent technology requires the ability to draw inferences from one instance, which means that the learned model can be transferred in similar scenarios, instead of training from scratch for each task. Based on the above requirements, transfer learning has gained more and more attention. [0003] The key to transfer learning tech...

AI Technical Summary

Problems solved by technology

However, behind the high accuracy rate is a large amount of high-quality labeling data. The reality is that the cost of data labeling is extremely expensive, which is unaffordable for some small companies and even some large companies.

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