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Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains.
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W. Pan, E.W. Xiang, N.N. Liu, and Q. Yang, “Transfer learning in collaborative filtering for sparsity reduction,” in Proc. 24th AAAI Conference on Artificial Intelligence
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2011
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2011
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2019
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C. Lu, F. Hu, D. Cao, J. Gong, Y. Xing, and Z. Li, “Transfer learning for driver model adaptation in lane-changing scenarios using manifold alignment,” IEEE Trans. Intell. Transp. Syst
2019
Closest in time.
Y. Liu, P. Lasang, S. Pranata, S. Shen, and W. Zhang, “Driver pose estimation using recurrent lightweight network and virtual data augmented transfer learning,” IEEE Trans. Intell. Transp. Syst
2019
Closest in time.
S. Bansod and A. Nandedkar, “Transfer learning for video anomaly detection,” J. Intell. Fuzzy Syst
2019
Closest in time.
G. Hu, Y. Zhang, and Q. Yang, “Transfer meets hybrid: A synthetic approach for cross-domain collaborative filtering with text,” in Proc. 28th International Conference on World Wide Web
2019
Closest in time.
F. Yuan, L. Yao, and B. Benatallah, “DARec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns,” in Proc. 29th International Joint Conference on Artificial Intelligence
2019
Closest in time.
U. Cote-Allard, C.L. Fall, A. Drouin, A. Campeau-Lecours, C. Gosselin, K. Glette, F. Laviolette, and B. Gosselin, “Deep learning for electromyographic hand gesture signal classification using transfer learning,” IEEE Trans. Neural Syst. Rehabil. Eng
2019
Closest in time.
D. Xi, F. Zhuang, G. Zhou, X. Cheng, F. Lin, and Q. He, “Domain adaptation with category attention network for deep sentiment analysis,” in Proc. The Web Conference
2020
Closest in time.
Y. Zhu, D. Xi, B. Song, F. Zhuang, S. Chen, X. Gu, and Q. He, “Modeling users’ behavior sequences with hierarchical explainable network for cross-domain fraud detection,” in Proc. The Web Conference
2020
Closest in time.
B. Sun, J. Feng, and K. Saenko, “Return of frustratingly easy domain adaptation,” in Proc. 30th AAAI Conference on Artificial Intelligence
2065
Closest in time.
B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition
2073
Closest in time.