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| Deep Transfer Active Learning Method Combining Source Domain Difference and Target Domain Uncertainty |
| LIU Dapeng1, CAO Yongfeng1, SU Caixia1, ZHANG Lun1 |
| 1. School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025 |
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Abstract Training deep neural network models comes with a heavy labeling cost. To reduce the cost, a deep transfer active learning method combining source domain and target domain is proposed. With the initial model transferred from source task, the current task samples with larger contribution to the model performance improvement are labeled by using a dynamical weighting combination of source domain difference and target domain uncertainty. Information extraction ratio(IER) is concretely defined in the specific case. An IER-based batch training strategy and a T&N batch training strategy are proposed to deal with model training process. The proposed method is tested on two cross-dataset transfer learning experiments. The results show that the transfer active learning method achieves good performance and reduces the cost of annotation effectively and the proposed strategies optimize the distribution of computing resources during the active learning process. Thus, the model learns more times from samples in the early phases and less times in the later and end phases.
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Received: 22 January 2021
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| Fund:Science and Technology Foundation of Guizhou Province(No.QKHJC[2018]1114) |
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Corresponding Authors:
CAO Yongfeng, Ph.D., professor. His research interests include pattern recognition.
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About author:: LIU Dapeng, master student. His research interests include image processing and machine vision.
SU Caixia, master, lecturer. Her research interests include remote sensing image processing.
ZHANG Lun, master student. His research interests include image processing and machine vision. |
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2021, Vol. 34 
