摘要: The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment. A research …
arxiv.org
harvard.edu参考文章(311)
Ming Shao, Yun Fu, Sheng Li, Cross-view projective dictionary learning for person re-identification international conference on artificial intelligence. pp. 2155- 2161 ,(2015)
Simon Tong, Daphne Koller, Support Vector Machine Active Learning with Application sto Text Classification international conference on machine learning. pp. 999- 1006 ,(2000)
Kate Saenko, Eric Tzeng, Judy Hoffman, Trevor Darrell, Ning Zhang, Deep Domain Confusion: Maximizing for Domain Invariance computer vision and pattern recognition. ,(2014)
Pietro Perona, Gregory Griffin, Alex Holub, Caltech-256 Object Category Dataset California Institute of Technology. ,(2007)
Hugo Larochelle, François Laviolette, Mario Marchand, Pascal Germain, Hana Ajakan, Domain-Adversarial Neural Networks arXiv: Machine Learning. ,(2014)
Avrim Blum, Shuchi Chawla, Learning from Labeled and Unlabeled Data using Graph Mincuts international conference on machine learning. pp. 19- 26 ,(2001) , 10.1184/R1/6606860.V1
Nicolas Courty, Remi Flamary, Devis Tuia, Alain Rakotomamonjy, Optimal Transport for Domain Adaptation IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 39, pp. 1853- 1865 ,(2017) , 10.1109/TPAMI.2016.2615921
Arthur Gretton, Olivier Bousquet, Alex Smola, Bernhard Schölkopf, Measuring Statistical Dependence with Hilbert-Schmidt Norms Lecture Notes in Computer Science. pp. 63- 77 ,(2005) , 10.1007/11564089_7
Qiang Qiu, Rama Chellappa, Compositional Dictionaries for Domain Adaptive Face Recognition IEEE Transactions on Image Processing. ,vol. 24, pp. 5152- 5165 ,(2015) , 10.1109/TIP.2015.2479456
Karen Simonyan, Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition computer vision and pattern recognition. ,(2014)