TY - GEN
T1 - Instance-based deep transfer learning
AU - Wang, Tianyang
AU - Huan, Jun
AU - Zhu, Michelle
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/4
Y1 - 2019/3/4
N2 - Deep transfer learning recently has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain. Model-based deep transfer learning is probably the most frequently used method. However, very little research work has been devoted to enhancing deep transfer learning by focusing on the influence of data. In this paper, we propose an instance-based approach to improve deep transfer learning in a target domain. Specifically, we choose a pre-trained model from a source domain and apply this model to estimate the influence of training samples in a target domain. Then we optimize the training data of the target domain by removing the training samples that will lower the performance of the pre-trained model. We later either fine-tune the pre-trained model with the optimized training data in the target domain, or build a new model which is initialized partially based on the pre-trained model, and fine-tune it with the optimized training data in the target domain. Using this approach, transfer learning can help deep learning models to capture more useful features. Extensive experiments demonstrate the effectiveness of our approach on boosting the quality of deep learning models for some common computer vision tasks, such as image classification.
AB - Deep transfer learning recently has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain. Model-based deep transfer learning is probably the most frequently used method. However, very little research work has been devoted to enhancing deep transfer learning by focusing on the influence of data. In this paper, we propose an instance-based approach to improve deep transfer learning in a target domain. Specifically, we choose a pre-trained model from a source domain and apply this model to estimate the influence of training samples in a target domain. Then we optimize the training data of the target domain by removing the training samples that will lower the performance of the pre-trained model. We later either fine-tune the pre-trained model with the optimized training data in the target domain, or build a new model which is initialized partially based on the pre-trained model, and fine-tune it with the optimized training data in the target domain. Using this approach, transfer learning can help deep learning models to capture more useful features. Extensive experiments demonstrate the effectiveness of our approach on boosting the quality of deep learning models for some common computer vision tasks, such as image classification.
UR - http://www.scopus.com/inward/record.url?scp=85063574777&partnerID=8YFLogxK
U2 - 10.1109/WACV.2019.00045
DO - 10.1109/WACV.2019.00045
M3 - Conference contribution
AN - SCOPUS:85063574777
T3 - Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
SP - 367
EP - 375
BT - Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019
Y2 - 7 January 2019 through 11 January 2019
ER -