TY - JOUR
T1 - Improving the Generalization Ability of Deep Neural Networks for Cross-Domain Visual Recognition
AU - Zheng, Jianwei
AU - Lu, Chao
AU - Hao, Cong
AU - Chen, Deming
AU - Guo, Donghui
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2021/9
Y1 - 2021/9
N2 - Feature learning with deep neural networks (DNNs) has made remarkable progress in recent years. However, its data-driven nature makes the collection of labeled training data expensive or impossible when the testing domain changes. Here, we propose a method of transferable feature learning and instance-level adaptation to improve the generalization ability of DNNs so as to mitigate the domain shift challenge for cross-domain visual recognition. When less labeled information is available, our proposed method shows attractive results in the new target domain and outperforms the typical fine-tuning method. Two DNNs are chosen as the representatives working with our proposed method, to do a comprehensive study about the generalization ability on the tasks of image-to-image transfer, image-to-video transfer, multidomain image classification, and weakly supervised detection. The experimental results show that our proposed method is superior to other existing works in the literature. In addition, a large scale of cross-domain database is merged from three different domains, providing a quantitative platform to evaluate different approaches in the field of cross-domain object detection.
AB - Feature learning with deep neural networks (DNNs) has made remarkable progress in recent years. However, its data-driven nature makes the collection of labeled training data expensive or impossible when the testing domain changes. Here, we propose a method of transferable feature learning and instance-level adaptation to improve the generalization ability of DNNs so as to mitigate the domain shift challenge for cross-domain visual recognition. When less labeled information is available, our proposed method shows attractive results in the new target domain and outperforms the typical fine-tuning method. Two DNNs are chosen as the representatives working with our proposed method, to do a comprehensive study about the generalization ability on the tasks of image-to-image transfer, image-to-video transfer, multidomain image classification, and weakly supervised detection. The experimental results show that our proposed method is superior to other existing works in the literature. In addition, a large scale of cross-domain database is merged from three different domains, providing a quantitative platform to evaluate different approaches in the field of cross-domain object detection.
KW - Cross-domain
KW - deep neural networks (DNNs)
KW - domain adaptation
KW - generalization ability
KW - visual recognition
UR - http://www.scopus.com/inward/record.url?scp=85078022024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078022024&partnerID=8YFLogxK
U2 - 10.1109/TCDS.2020.2965166
DO - 10.1109/TCDS.2020.2965166
M3 - Article
AN - SCOPUS:85078022024
SN - 2379-8920
VL - 13
SP - 607
EP - 620
JO - IEEE Transactions on Cognitive and Developmental Systems
JF - IEEE Transactions on Cognitive and Developmental Systems
IS - 3
M1 - 8954791
ER -