A. Achille, M. Lam, R. Tewari, A. Ravichandran, S. Maji et al., Task embedding for meta-learning, 2019.

Y. Amit, M. Fink, N. Srebro, and S. Ullman, Uncovering shared structures in multiclass classification, Proceedings of the 24th international conference on Machine learning, pp.17-24, 2007.

R. K. Ando and T. Zhang, A framework for learning predictive structures from multiple tasks and unlabeled data, Journal of Machine Learning Research, vol.6, pp.1817-1853, 2005.

M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau et al., Learning to learn by gradient descent by gradient descent, Advances in neural information processing systems, pp.3981-3989, 2016.

A. Argyriou, T. Evgeniou, and M. Pontil, Multi-task feature learning, Advances in neural information processing systems, pp.41-48, 2007.

Y. Aytar, A. Zisserman, . Tabula, and . Rasa, Model transfer for object category detection, Proc. 2011 Int. Conf. Computer Vision, pp.2252-2259, 2011.

M. Baktashmotlagh, M. T. Harandi, B. C. Lovell, and M. Salzmann, Domain adaptation on the statistical manifold, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2481-2488, 2014.

S. Ben-david, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira et al., A theory of learning from different domains, Machine learning, vol.79, pp.151-175, 2010.

Y. Bengio, A. Courville, and P. Vincent, Representation learning: A review and new perspectives, IEEE transactions, vol.35, pp.1798-1828, 2013.

H. S. Bhatt, A. Rajkumar, R. , and S. , Multi-source iterative adaptation for cross-domain classification, IJCAI, pp.3691-3697, 2016.

M. C. Burl and P. Perona, Recognition of planar object classes, Computer Vision and Pattern Recognition, pp.223-230, 1996.

P. P. Busto and J. Gall, Open set domain adaptation, The IEEE International Conference on Computer Vision (ICCV, vol.1, p.3, 2017.

Z. Cao, M. Long, J. Wang, J. , and M. I. , Partial transfer learning with selective adversarial networks, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2018.

M. Chen, Z. Xu, K. Q. Weinberger, S. , and F. , Marginalized denoising autoencoders for domain adaptation, Proceedings of the 29th International Coference on International Conference on Machine Learning, pp.1627-1634, 2012.

T. Chen, I. Goodfellow, J. Shlens, and . Net2net, Accelerating learning via knowledge transfer, 2015.

Y. Chen, J. Wang, M. Huang, Y. , and H. , Cross-position activity recognition with stratified transfer learning, Pervasive and Mobile Computing, vol.57, pp.1-13, 2019.

Z. Chen and B. Liu, Lifelong machine learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.10, pp.1-145, 2016.

E. Collier, R. Dibiano, S. Mukhopadhyay, and . Cactusnets, Layer applicability as a metric for transfer learning, International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2018.

N. Courty, R. Flamary, A. Habrard, and A. Rakotomamonjy, Joint distribution optimal transportation for domain adaptation, Advances in Neural Information Processing Systems, pp.3733-3742, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01620589

N. Courty, R. Flamary, and D. Tuia, Domain adaptation with regularized optimal transport, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.274-289, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01018698

N. Courty, R. Flamary, D. Tuia, and A. Rakotomamonjy, Optimal transport for domain adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01103073

M. Cuturi, Sinkhorn distances: Lightspeed computation of optimal transport, Advances in neural information processing systems, pp.2292-2300, 2013.

W. Dai, Q. Yang, G. Xue, Y. , and Y. , Boosting for transfer learning, Proc. 24th Int. Conf. Machine Learning, pp.193-200, 2007.

O. Day and T. M. Khoshgoftaar, A survey on heterogeneous transfer learning, Journal of Big Data, vol.4, p.29, 2017.

Z. Ding and Y. Fu, Robust transfer metric learning for image classification, IEEE Transactions on Image Processing, vol.26, pp.660-670, 2017.

C. Doersch, A. Gupta, and A. A. Efros, Unsupervised visual representation learning by context prediction, Proceedings of the IEEE International Conference on Computer Vision, pp.1422-1430, 2015.

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang et al., Decaf: A deep convolutional activation feature for generic visual recognition, International conference on machine learning, pp.647-655, 2014.

J. Donahue, P. Krähenbühl, D. , and T. , Adversarial feature learning, 2016.

Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever et al., Rl? 2: Fast reinforcement learning via slow reinforcement learning, 2016.

K. Dwivedi and G. Roig, Representation similarity analysis for efficient task taxonomy & transfer learning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.12387-12396, 2019.

D. Erhan, Y. Bengio, A. Courville, P. Manzagol, P. Vincent et al., Why does unsupervised pre-training help deep learning, Journal of Machine Learning Research, vol.11, pp.625-660, 2010.

L. Fei-fei, R. Fergus, and P. Perona, One-shot learning of object categories, IEEE transactions, vol.28, issue.4, pp.594-611, 2006.

B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, Unsupervised visual domain adaptation using subspace alignment, IEEE International Conference on Computer Vision, ICCV 2013, pp.2960-2967, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00869417

M. Fink, Object classification from a single example utilizing class relevance metrics, Advances in neural information processing systems, pp.449-456, 2005.

C. Finn, P. Abbeel, and S. Levine, Model-agnostic meta-learning for fast adaptation of deep networks, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.1126-1135, 2017.

C. Finn, S. Levine, A. , and P. , Guided cost learning: Deep inverse optimal control via policy optimization, International Conference on Machine Learning, pp.49-58, 2016.

C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine et al., Deep spatial autoencoders for visuomotor learning, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.512-519, 2016.

C. Finn, T. Yu, J. Fu, P. Abbeel, and S. Levine, Generalizing skills with semi-supervised reinforcement learning, 2016.

C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine, One-shot visual imitation learning via meta-learning, 2017.

M. Friedjungová and M. Jirina, Asymmetric heterogeneous transfer learning: A survey, DATA, pp.17-27, 2017.

Y. Ganin and V. Lempitsky, Unsupervised domain adaptation by backpropagation, International Conference on Machine Learning, pp.1180-1189, 2015.

Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle et al., Domain-adversarial training of neural networks, The Journal of Machine Learning Research, vol.17, pp.2096-2030, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01624607

L. A. Gatys, A. S. Ecker, and M. Bethge, A neural algorithm of artistic style, 2015.

L. Ge, J. Gao, H. Ngo, K. Li, and A. Zhang, On handling negative transfer and imbalanced distributions in multiple source transfer learning. Statistical Analysis and Data Mining, The ASA Data Science Journal, vol.7, pp.254-271, 2014.

W. Ge, Y. , and Y. , Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning, Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol.6, 2017.

B. Gong, Y. Shi, F. Sha, and K. Grauman, Geodesic flow kernel for unsupervised domain adaptation, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.2066-2073, 2012.

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, Advances in neural information processing systems, pp.2672-2680, 2014.

R. Gopalan, R. Li, and R. Chellappa, Domain adaptation for object recognition: An unsupervised approach, 2011 international conference on computer vision, pp.999-1006, 2011.

A. Gretton, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil et al., Optimal kernel choice for large-scale two-sample tests, Advances in neural information processing systems, pp.1205-1213, 2012.

D. Ha, A. Dai, Q. V. Le, and . Hypernetworks, , 2016.

K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, Momentum contrast for unsupervised visual representation learning, 2019.

G. Hinton, O. Vinyals, and J. Dean, Distilling the knowledge in a neural network, 2015.

R. D. Hjelm, A. Fedorov, S. Lavoie-marchildon, K. Grewal, P. Bachman et al., Learning deep representations by mutual information estimation and maximization, International Conference on Learning Representations, 2019.

S. Hochreiter, A. S. Younger, and P. R. Conwell, Learning to learn using gradient descent, International Conference on Artificial Neural Networks, pp.87-94, 2001.

J. Hoffman, E. Tzeng, T. Park, J. Zhu, P. Isola et al., Cycle-consistent adversarial domain adaptation, 2017.

M. Jaderberg, V. Mnih, W. M. Czarnecki, T. Schaul, J. Z. Leibo et al., Reinforcement learning with unsupervised auxiliary tasks, 2016.

W. Jiang, E. Zavesky, S. Chang, and A. Loui, Cross-domain learning methods for high-level visual concept classification, 15th IEEE International Conference on, pp.161-164, 2008.

A. Karbalayghareh, X. Qian, and E. R. Dougherty, Optimal bayesian transfer learning, IEEE Transactions on Signal Processing, vol.66, pp.3724-3739, 2018.

M. Kempka, M. Wydmuch, G. Runc, J. Toczek, J. et al., Vizdoom: A doom-based ai research platform for visual reinforcement learning, 2016 IEEE Conference on Computational Intelligence and Games (CIG), pp.1-8, 2016.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

I. Kuzborskij and F. Orabona, Stability and hypothesis transfer learning, International Conference on Machine Learning, pp.942-950, 2013.

I. Kuzborskij, F. Orabona, and B. Caputo, From n to n+1: Multiclass transfer incremental learning, Proc. 2013 IEEE Conf. Computer Vision and Pattern Recognition, pp.3358-3365, 2013.

C. H. Lampert, H. Nickisch, and S. Harmeling, Attribute-based classification for zero-shot visual object categorization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, pp.453-465, 2013.

H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Proceedings of the 26th annual international conference on machine learning, pp.609-616, 2009.

H. Lee, P. Pham, Y. Largman, and A. Y. Ng, Unsupervised feature learning for audio classification using convolutional deep belief networks, Advances in neural information processing systems, pp.1096-1104, 2009.

K. Li, M. , and J. , , 2016.

S. Li, K. Li, and Y. Fu, Self-taught low-rank coding for visual learning, IEEE Trans. Neural Netw. Learn. Syst, 2017.

X. Li, Regularized adaptation: Theory, algorithms and applications, vol.68, 2007.

M. Liu and O. Tuzel, Coupled generative adversarial networks, Advances in neural information processing systems, pp.469-477, 2016.

M. Long, J. Wang, G. Ding, J. Sun, Y. et al., Transfer feature learning with joint distribution adaptation, 2013 IEEE Int. Conf. Computer Vision, pp.2200-2207, 2013.

M. Long, H. Zhu, J. Wang, J. , and M. I. , Unsupervised domain adaptation with residual transfer networks, Adv. Neural Inf. Process Syst, 2016.

M. Long, H. Zhu, J. Wang, J. , and M. I. , Deep transfer learning with joint adaptation networks, Proceedings of the 34th International Conference on Machine Learning (International Convention Centre, vol.70, pp.2208-2217, 2017.

M. Long, Y. Cao, J. Wang, J. , and M. , Learning transferable features with deep adaptation networks, Proc. 32nd Int. Conf. Machine Learning, pp.97-105, 2015.

H. Lu, L. Zhang, Z. Cao, W. Wei, K. Xian et al., When unsupervised domain adaptation meets tensor representations, The IEEE International Conference on Computer Vision (ICCV, vol.2, 2017.

Y. Lu, L. Chen, A. Saidi, E. Dellandrea, W. et al., Discriminative transfer learning using similarities and dissimilarities, IEEE Transactions on Neural Networks and Learning Systems, vol.29, issue.7, pp.3097-3110, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02070137

Z. Lu, Y. Zhu, S. J. Pan, E. W. Xiang, Y. Wang et al., Source free transfer learning for text classification, Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.

L. Luo, L. Chen, S. Hu, Y. Lu, W. et al., Discriminative and geometry aware unsupervised domain adaptation, 2017.

Y. Luo, Y. Wen, L. Duan, and D. Tao, Transfer metric learning: Algorithms, applications and outlooks, 2018.

V. Manjunatha, S. Ramalingam, T. K. Marks, D. , and L. , Class subset selection for transfer learning using submodularity, 2018.

T. Munkhdalai, Y. , and H. M. Networks, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.2554-2563, 2017.

D. K. Naik and R. J. Mammone, Meta-neural networks that learn by learning, Proceedings 1992] IJCNN International Joint Conference on Neural Networks, vol.1, pp.437-442, 1992.

A. Nichol, J. Achiam, and J. Schulman, On first-order meta-learning algorithms, 2018.

M. Noroozi and P. Favaro, Unsupervised learning of visual representations by solving jigsaw puzzles, European Conference on Computer Vision, pp.69-84, 2016.

M. Noroozi, H. Pirsiavash, and P. Favaro, Representation learning by learning to count, Proceedings of the IEEE International Conference on Computer Vision, pp.5898-5906, 2017.

A. Pal and V. N. Balasubramanian, Zero-shot task transfer, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

S. J. Pan, Transfer learning, Data Classification: Algorithms and, pp.537-570, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01934907

S. J. Pan, J. T. Kwok, Y. , and Q. , Transfer learning via dimensionality reduction, AAAI, vol.8, pp.677-682, 2008.

S. J. Pan, I. W. Tsang, J. T. Kwok, Y. , and Q. , Domain adaptation via transfer component analysis, IEEE Transactions on Neural Networks, vol.22, pp.199-210, 2011.

S. J. Pan, Y. , and Q. , A survey on transfer learning, IEEE Trans. Knowl. Data Eng, vol.22, pp.1345-1359, 2010.

S. Parameswaran and K. Q. Weinberger, Large margin multi-task metric learning, Advances in neural information processing systems, pp.1867-1875, 2010.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, Context encoders: Feature learning by inpainting, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.2536-2544, 2016.

M. Perrot, N. Courty, R. Flamary, and A. Habrard, Mapping estimation for discrete optimal transport, Advances in Neural Information Processing Systems, pp.4197-4205, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01376970

M. Perrot and A. Habrard, A theoretical analysis of metric hypothesis transfer learning, International Conference on Machine Learning, pp.1708-1717, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01175610

G. Qi, C. Aggarwal, Y. Rui, Q. Tian, S. Chang et al., Towards cross-category knowledge propagation for learning visual concepts, Proc. 2011 IEEE Conf. Computer Vision and Pattern Recognition, pp.897-904, 2011.

A. Quattoni, M. Collins, D. , and T. , Learning visual representations using images with captions, Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pp.1-8, 2007.

R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng, Self-taught learning: Transfer learning from unlabeled data, Proc. 24th Int. Conf. Machine Learning, 2007.

S. Ravi and H. Larochelle, Optimization as a model for few-shot learning, ICLR, 2017.

A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta et al., Hints for thin deep nets, 2014.

M. T. Rosenstein, Z. Marx, L. P. Kaelbling, and T. G. Dietterich, To transfer or not to transfer, NIPS 2005 workshop on transfer learning, vol.898, p.3, 2005.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision (IJCV), vol.115, pp.211-252, 2015.

R. Salakhutdinov, J. Tenenbaum, and A. Torralba, One-shot learning with a hierarchical nonparametric bayesian model, Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp.195-206, 2012.

A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, Meta-learning with memory-augmented neural networks, International conference on machine learning, pp.1842-1850, 2016.

J. Schmidhuber, Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook. PhD thesis, 1987.

J. Schmidhuber, Learning to control fast-weight memories: An alternative to dynamic recurrent networks, Neural Computation, vol.4, pp.131-139, 1992.

C. Seah, Y. Ong, and I. W. Tsang, Combating negative transfer from predictive distribution differences, IEEE transactions on cybernetics, vol.43, pp.1153-1165, 2012.

L. Shao, F. Zhu, L. , and X. , Transfer learning for visual categorization: A survey, IEEE Trans. Neural Netw. Learn. Syst. PP, pp.1-1, 2014.

A. Sharif-razavian, H. Azizpour, J. Sullivan, C. , and S. , Cnn features off-the-shelf: an astounding baseline for recognition, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp.806-813, 2014.

S. Si, D. Tao, and B. Geng, Bregman divergence-based regularization for transfer subspace learning, IEEE Trans. Knowl. Data Eng, vol.22, pp.929-942, 2010.

D. L. Silver and K. P. Bennett, Guest editor's introduction: special issue on inductive transfer learning, Machine Learning, vol.73, pp.215-220, 2008.

D. L. Silver, Q. Yang, L. , and L. , Lifelong machine learning systems: Beyond learning algorithms, AAAI spring symposium series, 2013.

S. Srinivas and F. Fleuret, Knowledge transfer with Jacobian matching, Proceedings of the 35th International Conference on Machine Learning, vol.80, pp.4730-4738, 2018.

T. Standley, A. R. Zamir, D. Chen, L. Guibas, J. Malik et al., Which tasks should be learned together in multi-task learning?, 2019.

B. Sun and K. Saenko, Subspace distribution alignment for unsupervised domain adaptation, BMVC, pp.24-25, 2015.

F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr et al., Learning to compare: Relation network for few-shot learning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1199-1208, 2018.

Y. Tang, J. Wang, X. Wang, B. Gao, E. Dellandréa et al., Visual and semantic knowledge transfer for large scale semi-supervised object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01678123

C. Tessler, S. Givony, T. Zahavy, D. J. Mankowitz, and S. Mannor, A deep hierarchical approach to lifelong learning in minecraft, Thirty-First AAAI Conference on Artificial Intelligence, 2017.

S. Thrun, Is learning the n-th thing any easier than learning the first?, Advances in Neural Information Processing Systems, pp.640-646, 1996.

S. Thrun and L. Pratt, Learning to learn, 2012.

T. Tommasi, F. Orabona, and B. Caputo, Safety in numbers: Learning categories from few examples with multi model knowledge transfer, Proc. 2010 IEEE Conf. Computer Vision and Pattern Recognition, pp.3081-3088, 2010.

E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko, Simultaneous deep transfer across domains and tasks, Proceedings of the IEEE International Manuscript submitted to ACM Conference on Computer Vision, pp.4068-4076, 2015.

E. Tzeng, J. Hoffman, K. Saenko, D. , and T. , Adversarial discriminative domain adaptation, Computer Vision and Pattern Recognition (CVPR, vol.1, p.4, 2017.

E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, D. et al., Deep domain confusion: Maximizing for domain invariance, 2014.

V. Vapnik, Principles of risk minimization for learning theory, Advances in neural information processing systems, pp.831-838, 1992.

C. Villani, Optimal transport: old and new, vol.338, 2008.

P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol, Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning, pp.1096-1103, 2008.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, vol.11, pp.3371-3408, 2010.

H. Wang, F. Nie, and H. Huang, Robust and discriminative self-taught learning, International Conference on Machine Learning, pp.298-306, 2013.

J. Wang, Y. Chen, S. Hao, W. Feng, and Z. Shen, Balanced distribution adaptation for transfer learning, 2017 IEEE International Conference on Data Mining (ICDM, pp.1129-1134, 2017.

J. Wang, W. Feng, Y. Chen, H. Yu, M. Huang et al., Visual domain adaptation with manifold embedded distribution alignment, 2018 ACM Multimedia Conference on Multimedia Conference, pp.402-410, 2018.

J. Wang, V. W. Zheng, Y. Chen, and M. Huang, Deep transfer learning for cross-domain activity recognition, Proceedings of the 3rd International Conference on Crowd Science and Engineering, p.16, 2018.

M. Wang and W. Deng, Deep visual domain adaptation: A survey, Neurocomputing, vol.312, pp.135-153, 2018.

K. Weiss, T. M. Khoshgoftaar, W. , and D. , A survey of transfer learning, Journal of Big data, vol.3, p.9, 2016.

Z. Wu, Y. Xiong, S. X. Yu, L. , and D. , Unsupervised feature learning via non-parametric instance discrimination, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3733-3742, 2018.

E. W. Xiang, S. J. Pan, W. Pan, J. Su, Y. et al., Source-selection-free transfer learning, Twenty-Second International Joint Conference on Artificial Intelligence, 2011.

J. Yang, R. Yan, and A. G. Hauptmann, Cross-domain video concept detection using adaptive svms, Proc. 15th Int. Conf. Multimedia, pp.188-197, 2007.

Y. Yao and G. Doretto, Boosting for transfer learning with multiple sources, Proc. 2010 IEEE Conf. Computer Vision and Pattern Recognition, pp.1855-1862, 2010.

J. Yim, D. Joo, J. Bae, K. , and J. , A gift from knowledge distillation: Fast optimization, network minimization and transfer learning, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2017.

W. Ying, Y. Zhang, J. Huang, Y. , and Q. , Transfer learning via learning to transfer, International Conference on Machine Learning, pp.5072-5081, 2018.

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, How transferable are features in deep neural networks?, Advances in neural information processing systems, pp.3320-3328, 2014.

X. Yu, A. , and Y. , Attribute-based transfer learning for object categorization with zero/one training example, Computer Vision-ECCV 2010, pp.127-140, 2010.

A. R. Zamir, A. Sax, W. Shen, L. J. Guibas, J. Malik et al., Disentangling task transfer learning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3712-3722, 2018.

A. R. Zamir, T. Wekel, P. Agrawal, C. Wei, J. Malik et al., Generic 3d representation via pose estimation and matching, European Conference on Computer Vision, pp.535-553, 2016.

J. Zhang, Z. Ding, W. Li, and P. Ogunbona, Importance weighted adversarial nets for partial domain adaptation, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2018.

J. Zhang, W. Li, and P. Ogunbona, Joint geometrical and statistical alignment for visual domain adaptation, 2017.

J. Zhang, W. Li, P. Ogunbona, and D. Xu, Recent advances in transfer learning for cross-dataset visual recognition: A problem-oriented perspective, ACM Computing Surveys (CSUR), vol.52, p.7, 2019.

L. Zhang, Transfer adaptation learning: A decade survey, 2019.

R. Zhang, P. Isola, and A. A. Efros, Colorful image colorization, European conference on computer vision, pp.649-666, 2016.

Y. Zhang, Y. , and Q. , A survey on multi-task learning, 2017.

B. Zhao, Y. Fu, R. Liang, J. Wu, Y. Wang et al., A large-scale attribute dataset for zero-shot learning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.0-0, 2019.

C. Zhuang, A. L. Zhai, Y. , and D. , Local aggregation for unsupervised learning of visual embeddings, Proceedings of the IEEE International Conference on Computer Vision, pp.6002-6012, 2019.

F. Zohrizadeh, M. Kheirandishfard, and F. Kamangar, Class subset selection for partial domain adaptation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019.