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Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase.
Mixture regression for covariate shift
Sugiyama, M., Storkey, A.J.: · 2007
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2007
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Knowledge transfer via distillation of activation boundaries formed by hidden neurons
Heo, B., Lee, M., Yun, S., Choi, J.Y.: · 2007
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Visualizing data using t-sne
Maaten, L.v.d., Hinton, G.: · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Periocular region appearance cues for biometric identification
Woodard, D.L., Pundlik, S.J., Lyle, J.R., Miller, P.E.: · 2010
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Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: · 2010
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HMDB: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: · 2011
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2012
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A.R., Shah, M.: · 2012
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Deep domain confusion: Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Unsupervised domain adaptation by backpropagation
Ganin, Y., Lempitsky, V.: · 2015
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A personal credit rating prediction model using data mining in smart ubiquitous environments
Bae, J.K., Kim, J.: · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., Jordan, M.I.: · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
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Deep visual-semantic alignments for generating image descriptions
Karpathy, A., Fei-Fei, L.: · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., Dean, J.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Fusing iris, palmprint and fingerprint in a multi-biometric recognition system
Naderi, H., Soleimani, B.H., Matwin, S., Araabi, B.N., Soltanian-Zadeh, H.: · 2016
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Unsupervised domain adaptation with residual transfer networks
Long, M., Zhu, H., Wang, J., Jordan, M.I.: · 2016
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Domain separation networks
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2017
Learning semantic representations for unsupervised domain adaptation
Xie, S., Zheng, Z., Chen, L., Chen, C.: · 2018
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Multi-adversarial domain adaptation
Pei, Z., Cao, Z., Long, M., Wang, J.: · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: · 2018
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Fine-tuning cnn image retrieval with no human annotation
Radenović, F., Tolias, G., Chum, O.: · 2018
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Knowledge transfer with Jacobian matching
Srinivas, S., Fleuret, F.: · 2018
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Patch-level augmentation for object detection in aerial images
Hong, S., Kang, S., Cho, D.: · 2019
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Federated multi-task learning
Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.S.: · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: · 2017
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Visda: The visual domain adaptation challenge
Peng, X., Usman, B., Kaushik, N., Hoffman, J., Wang, D., Saenko, K.: · 2017
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Central moment discrepancy (cmd) for domain-invariant representation learning
Zellinger, W., Grubinger, T., Lughofer, E., Natschläger, T., Saminger-Platz, S.: · 2017
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Adversarial discriminative domain adaptation
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: · 2017
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The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., Suleyman, M., Zisserman, A.: · 2017
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Cho, D., Hong, S., Kang, S., Kim, J.: · 2019
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Slowfast networks for video recognition
Feichtenhofer, C., Fan, H., Malik, J., He, K.: · 2019
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Cnn-based semantic segmentation using level set loss
Kim, Y., Kim, S., Kim, T., Kim, C.: · 2019
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Bilinear siamese networks with background suppression for visual object tracking
Lee, H., Choi, S., Kim, Y., Kim, C.: · 2019
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Unsupervised face domain transfer for low-resolution face recognition
Hong, S., Ryu, J.: · 2019
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Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., Tong, Y.: · 2019
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Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation
Xu, R., Li, G., Yang, J., Lin, L.: · 2019
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Domain-specific batch normalization for unsupervised domain adaptation
Chang, W.G., You, T., Seo, S., Kwak, S., Han, B.: · 2019
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Progressive domain adaptation for object detection
Hsu, H.K., Yao, C.H., Tsai, Y.H., Hung, W.C., Tseng, H.Y., Singh, M., Yang, M.H.: · 2020
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Hi-cmd: Hierarchical cross-modality disentanglement for visible-infrared person re-identification
Choi, S., Lee, S., Kim, Y., Kim, T., Kim, C.: · 2020
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Attention-guided adaptation factors for unsupervised facial domain adaptation
Hong, S., Ryu, J.: · 2020
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