期刊
COGNITIVE SYSTEMS RESEARCH
卷 45, 期 -, 页码 109-123出版社
ELSEVIER
DOI: 10.1016/j.cogsys.2017.05.006
关键词
Active Learning (AL); Semi-Supervised Learning (SSL); Convolutional Neural Network (CNN); Deep learning; Object detection
资金
- Inha University research grant
In this paper, we address the combination of the active learning (AL) and semi-supervised (SSL) learnings, called ASSL, to leverage the strong points of the both learning paradigms for improving the performance of object detection. Considering the pros and cons of the AL and SSL learning methods, ASSL where SSL method provides the incremental improvement of semi-supervised detection performance by combining the concept of diversity imported from AL methods. The proposed method demonstrates outstanding performance compared with state-of-art methods on the challenging Caltech pedestrian detection dataset, reducing the miss rate to 12.2%, which is significantly smaller than current state-of-art. In addition, extensive experiments have been carried out using ILSVRC detection dataset and online evaluation for activity recognition. (C) 2017 Elsevier B.V. All rights reserved.
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