A domain adaptation deep transfer method for image classification

Yu Chen; Chunling Yang; Yan Zhang; Yuze Li

doi:10.1117/12.2509230

7 March 2019 A domain adaptation deep transfer method for image classification

Yu Chen, Chunling Yang, Yan Zhang, Yuze Li

Proceedings Volume 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation; 1105315 (2019) https://doi.org/10.1117/12.2509230
Event: 10th International Symposium on Precision Engineering Measurements and Instrumentation (ISPEMI 2018), 2018, Kunming, China

Abstract

The deep learning models have recently shown outstanding performance in many computer vision applications. However, this superior performance requires a very large number of annotated image samples, pre-venting application to problems with limited training data. To overcome this limitation, we propose a Do-main Adaptation Deep Transfer Model (DADTM) in this paper. The DADTM improves the classical transfer models by the proposed domain invariance value metric and a domain invariance reconstruction, increasing the model transferability and enhancing the classification performance. The comparative experiments are performed to evaluate the DADTM-based classification algorithm. The results show that the proposed mod-el and algorithm outperform the traditional methods.

Citation Download Citation

Yu Chen, Chunling Yang, Yan Zhang, and Yuze Li "A domain adaptation deep transfer method for image classification", Proc. SPIE 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation, 1105315 (7 March 2019); https://doi.org/10.1117/12.2509230

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