Unsupervised Adaptation Across Domain Shifts by Generating Intermediate Data Representations
Tóm tắt
Từ khóa
Tài liệu tham khảo
lui, 0, Grassmann registration manifolds for face recognition, Proc 10th Eur Conf Comput Vis, 44
japkowicz, 2002, The class imbalance problem: A systematic study, Intell Data Anal, 6, 429, 10.3233/IDA-2002-6504
hoffman, 0, Discovering latent domains for multisource domain adaptation, Proc 12th Eur Conf Comput Vis, 702
jiang, 0, A literature survey on domain adaptation of statistical classifiers
jhuo, 2012, Robust visual domain adaptation with low-rank reconstruction, Proc IEEE Conf Comput Vis Pattern Recog, 2168
gong, 2012, Geodesic flow kernel for unsupervised domain adaptation, Proc IEEE Conf Comput Vis Pattern Recog, 2066
glorot, 2011, Domain adaptation for large-scale sentiment classification: A deep learning approach, Proc 28th Int Conf Mach Learn, 513
edelman, 1999, The geometry of algorithms with orthogonality constraints, SIAM J Matrix Anal Appl, 20, 303, 10.1137/S0895479895290954
duan, 2012, Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach, Proc IEEE Conf Comput Vis Pattern Recog, 1338
zheng, 2012, A grassmann manifold-based domain adaptation approach, Proc Int Conf Pattern Recog, 2095
xing, 2007, Bridged refinement for transfer learning, Proc 11th Eur Conf Principles Practice Knowl Discovery Databases, 324
wold, 1985, Partial least squares, Encyclopedia of Statistical Sciences
wang, 0, Manifold alignment without correspondence, Proc 21st Int Joint Conf Artif Intell, 1273
blitzer, 0, Learning bounds for domain adaptation, Proc Advances Neural Inf Process Syst, 129
blitzer, 0, Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification, Proc 45th Annu Meeting Assoc Comput Linguist, 440
mansour, 0, Domain adaptation: Learning bounds and algorithms
blitzer, 2011, Domain adaptation with coupled subspaces, Proc Artif Intell Statist, 173
chen, 2011, Co-Training for Domain Adaptation, Proc Advances Neural Inf Process Syst, 2456
chikuse, 0, Statistics on Special Manifolds
dai, 0, Transferring naive Bayes classifiers for text classification, Proc Nat Conf Artif Intell, 540
daumé iii, 0, Co-regularization based semi-supervised domain adaptation, Proc Advances Neural Inf Process Syst, 478
daumé iii, 0, Domain adaptation for statistical classifiers, J Artif Intell Res, 26, 101, 10.1613/jair.1872
ben-david, 2007, Analysis of representations for domain adaptation, Proc Advances Neural Inf Process Syst, 137
david, 2010, A theory of learning from different domains, Mach Learn, 79, 151, 10.1007/s10994-009-5152-4
benbouzid, 2012, Multiboost: A multi-purpose boosting package, J Mach Learn Res, 13, 549
ben-david, 0, Impossibility theorems for domain adaptation, Proc 13th Int Conf on Artificial Intell, 129
bergamo, 0, Exploiting weakly-labeled web images to improve object classification: A domain adaptation approach, Proc Advances Neural Inf Process Syst, 181
saenko, 0, Adapting visual category models to new domains, Proc 11th Eur Conf Comput Vis, 213
shi, 2012, Information-theoretical learning of discriminative clusters for unsupervised domain adaptation, Proc 29th Int Conf Mach Learn, 1079
mansour, 0, Domain adaptation with multiple sources, Proc Advances Neural Inf Process Syst, 1041