References

[1] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. 2014,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. 2014,&

[2] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012, 1097--1105. Google Scholar http://scholar.google.com/scholar_lookup?title=Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012, 1097--1105&

[3] Wang Y-X, Girshick R, Hebert M, et al. Low-shot learning from imaginary data. 2018,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Wang Y-X, Girshick R, Hebert M, et al. Low-shot learning from imaginary data. 2018,&

[4] Tan X, Chen S, Zhou Z H. Face recognition from a single image per person: A survey. Pattern Recognition, 2006, 39: 1725-1745 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=Face recognition from a single image per person: A survey&author=Tan X&author=Chen S&author=Zhou Z H&publication_year=2006&journal=Pattern Recognition&volume=39&pages=1725-1745

[5] Finn C, Yu T H, Zhang T H, et al. One-shot visual imitation learning via meta-learning. In: Proceedings of the 1st Annual Conference on Robot Learning, Mountain View, 2017. 357--368. Google Scholar http://scholar.google.com/scholar_lookup?title=Finn C, Yu T H, Zhang T H, et al. One-shot visual imitation learning via meta-learning. In: Proceedings of the 1st Annual Conference on Robot Learning, Mountain View, 2017. 357--368&

[6] Karlinsky L, Shtok J, Tzur Y, et al. Fine-grained recognition of thousands of object categories with single-example training. In: Proceedings of Conference on Computer Vision and Pattern Recognition, Honolulu, 2017. 965--974. Google Scholar http://scholar.google.com/scholar_lookup?title=Karlinsky L, Shtok J, Tzur Y, et al. Fine-grained recognition of thousands of object categories with single-example training. In: Proceedings of Conference on Computer Vision and Pattern Recognition, Honolulu, 2017. 965--974&

[7] Zhou Z H. Learnware: on the future of machine learning. Front Comput Sci, 2016, 10: 589-590 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=Learnware: on the future of machine learning&author=Zhou Z H&publication_year=2016&journal=Front Comput Sci&volume=10&pages=589-590

[8] Lake B M, Salakhutdinov R, Tenenbaum J B. Human-level concept learning through probabilistic program induction. Science, 2015, 350: 1332-1338 CrossRef PubMed ADS Google Scholar http://scholar.google.com/scholar_lookup?title=Human-level concept learning through probabilistic program induction&author=Lake B M&author=Salakhutdinov R&author=Tenenbaum J B&publication_year=2015&journal=Science&volume=350&pages=1332-1338

[9] Patricia N, Caputo B. Learning to learn, from transfer learning to domain adaptation: a unifying perspective. In: Proceedings of Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 1442--1449. Google Scholar http://scholar.google.com/scholar_lookup?title=Patricia N, Caputo B. Learning to learn, from transfer learning to domain adaptation: a unifying perspective. In: Proceedings of Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 1442--1449&

[10] Li Z G, Zhou F W, Chen F, et al. Meta-sgd: Learning to learn quickly for few shot learning. 2017,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Li Z G, Zhou F W, Chen F, et al. Meta-sgd: Learning to learn quickly for few shot learning. 2017,&

[11] Russakovsky O, Deng J, Su H. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis, 2015, 115: 211-252 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=ImageNet Large Scale Visual Recognition Challenge&author=Russakovsky O&author=Deng J&author=Su H&publication_year=2015&journal=Int J Comput Vis&volume=115&pages=211-252

[12] Koch G, Zemel R, Salakhutdinov R. Siamese neural networks for one-shot image recognition. In: Proceedings of the 32nd International Conference on MachineLearning, Lille, 2015. Google Scholar http://scholar.google.com/scholar_lookup?title=Koch G, Zemel R, Salakhutdinov R. Siamese neural networks for one-shot image recognition. In: Proceedings of the 32nd International Conference on MachineLearning, Lille, 2015&

[13] Vinyals O, Blundell C, Lillicrap T, et al. Matching networks for one shot learning. In: Proceedings of Advances in Neural Information Processing Systems, 2016. 3630--3638. Google Scholar http://scholar.google.com/scholar_lookup?title=Vinyals O, Blundell C, Lillicrap T, et al. Matching networks for one shot learning. In: Proceedings of Advances in Neural Information Processing Systems, 2016. 3630--3638&

[14] Snell J, Swersky K, Zemel R S. Prototypical networks for few-shot learning. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 4080--4090. Google Scholar http://scholar.google.com/scholar_lookup?title=Snell J, Swersky K, Zemel R S. Prototypical networks for few-shot learning. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 4080--4090&

[15] Triantafillou E, Zemel R S, Urtasun R. Few-shot learning through an information retrieval lens. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 2252--2262. Google Scholar http://scholar.google.com/scholar_lookup?title=Triantafillou E, Zemel R S, Urtasun R. Few-shot learning through an information retrieval lens. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 2252--2262&

[16] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of Proceedings of the 34th International Conference on Machine Learning, Sydney, 2017. 1126--1135. Google Scholar http://scholar.google.com/scholar_lookup?title=Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of Proceedings of the 34th International Conference on Machine Learning, Sydney, 2017. 1126--1135&

[17] Nichol A, Achiam J, Schulman J. On first-order meta-learning algorithms. 2018,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Nichol A, Achiam J, Schulman J. On first-order meta-learning algorithms. 2018,&

[18] Ravi S, Larochelle H. Optimization as a model for few-shot learning. In: Proceedings of International Conference on Learning Representations, 2017. Google Scholar http://scholar.google.com/scholar_lookup?title=Ravi S, Larochelle H. Optimization as a model for few-shot learning. In: Proceedings of International Conference on Learning Representations, 2017&

[19] Schölkopf B, Smola A J. Learning with Kernels. Cambridge: The MIT Press, 2001. Google Scholar http://scholar.google.com/scholar_lookup?title=Schölkopf B, Smola A J. Learning with Kernels. Cambridge: The MIT Press, 2001&

[20] Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. Berlin: Springer, 2001. Google Scholar http://scholar.google.com/scholar_lookup?title=Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. Berlin: Springer, 2001&

[21] Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation, 1997, 9: 1735-1780 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=Long Short-Term Memory&author=Hochreiter S&author=Schmidhuber J&publication_year=1997&journal=Neural Computation&volume=9&pages=1735-1780

[22] Schuster M, Paliwal K K. Bidirectional recurrent neural networks. IEEE Trans Signal Process, 1997, 45: 2673-2681 CrossRef ADS Google Scholar http://scholar.google.com/scholar_lookup?title=Bidirectional recurrent neural networks&author=Schuster M&author=Paliwal K K&publication_year=1997&journal=IEEE Trans Signal Process&volume=45&pages=2673-2681

[23] Zaheer M, Kottur S, Ravanbakhsh S, et al. Deep sets. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 3394--3404. Google Scholar http://scholar.google.com/scholar_lookup?title=Zaheer M, Kottur S, Ravanbakhsh S, et al. Deep sets. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 3394--3404&

[24] Bertinetto L, Henriques J F, Torr P H S, et al. Meta-learning with differentiable closed-form solvers. 2018,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Bertinetto L, Henriques J F, Torr P H S, et al. Meta-learning with differentiable closed-form solvers. 2018,&

[25] Li Fei-Fei , Fergus R, Perona P. One-shot learning of object categories.. IEEE Trans Pattern Anal Machine Intell, 2006, 28: 594-611 CrossRef PubMed Google Scholar http://scholar.google.com/scholar_lookup?title=One-shot learning of object categories.&author=Li Fei-Fei &author=Fergus R&author=Perona P&publication_year=2006&journal=IEEE Trans Pattern Anal Machine Intell&volume=28&pages=594-611

[26] Lake B M, Salakhutdinov R, Gross J, et al. One shot learning of simple visual concepts. In: Proceedings of the 33th Annual Meeting of the Cognitive Science Society, Boston, 2011. Google Scholar http://scholar.google.com/scholar_lookup?title=Lake B M, Salakhutdinov R, Gross J, et al. One shot learning of simple visual concepts. In: Proceedings of the 33th Annual Meeting of the Cognitive Science Society, Boston, 2011&

[27] Andrychowicz M, Denil M, Colmenarejo S G, et al. Learning to learn by gradient descent by gradient descent. In: Proceedings of Advances in Neural Information Processing Systems, 2016. 3981--3989. Google Scholar http://scholar.google.com/scholar_lookup?title=Andrychowicz M, Denil M, Colmenarejo S G, et al. Learning to learn by gradient descent by gradient descent. In: Proceedings of Advances in Neural Information Processing Systems, 2016. 3981--3989&

[28] Maurer A. Transfer bounds for linear feature learning. Mach Learn, 2009, 75: 327-350 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=Transfer bounds for linear feature learning&author=Maurer A&publication_year=2009&journal=Mach Learn&volume=75&pages=327-350

[29] Maurer A, Pontil M, Romera-Paredes B. The benefit of multitask representation learning. J Mach Learn Res, 2016, 17: 1--32. Google Scholar http://scholar.google.com/scholar_lookup?title=Maurer A, Pontil M, Romera-Paredes B. The benefit of multitask representation learning. J Mach Learn Res, 2016, 17: 1--32&

[30] Hariharan B, Girshick R B. Low-shot visual recognition by shrinking and hallucinating features. In: Proceedings of IEEE International Conference on Computer Vision, Venice, 2017. 3037--3046. Google Scholar http://scholar.google.com/scholar_lookup?title=Hariharan B, Girshick R B. Low-shot visual recognition by shrinking and hallucinating features. In: Proceedings of IEEE International Conference on Computer Vision, Venice, 2017. 3037--3046&

[31] Dai W Z, Muggleton S, Wen J, et al. Logical vision: one-shot meta-interpretive learning from real images. In: Proceedings of the 27th International Conference on Inductive Logic Programming, Orléans, 2017. 46--62. Google Scholar http://scholar.google.com/scholar_lookup?title=Dai W Z, Muggleton S, Wen J, et al. Logical vision: one-shot meta-interpretive learning from real images. In: Proceedings of the 27th International Conference on Inductive Logic Programming, Orléans, 2017. 46--62&

[32] Wang P, Liu L Q, Shen C H, et al. Multi-attention network for one shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017. 6212--6220. Google Scholar http://scholar.google.com/scholar_lookup?title=Wang P, Liu L Q, Shen C H, et al. Multi-attention network for one shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017. 6212--6220&

[33] Shyam P, Gupta S, Dukkipati A. Attentive recurrent comparators. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, 2017. 3173--3181. Google Scholar http://scholar.google.com/scholar_lookup?title=Shyam P, Gupta S, Dukkipati A. Attentive recurrent comparators. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, 2017. 3173--3181&

[34] Pan S J, Yang Q. A Survey on Transfer Learning. IEEE Trans Knowl Data Eng, 2010, 22: 1345-1359 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=A Survey on Transfer Learning&author=Pan S J&author=Yang Q&publication_year=2010&journal=IEEE Trans Knowl Data Eng&volume=22&pages=1345-1359

[35] Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer learning. J Big Data, 2016, 3: 9 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=A survey of transfer learning&author=Weiss K&author=Khoshgoftaar T M&author=Wang D D&publication_year=2016&journal=J Big Data&volume=3&pages=9

[36] Day O, Khoshgoftaar T M. A survey on heterogeneous transfer learning. J Big Data, 2017, 4: 29 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=A survey on heterogeneous transfer learning&author=Day O&author=Khoshgoftaar T M&publication_year=2017&journal=J Big Data&volume=4&pages=29

[37] Wang Y X, Ramanan D, Hebert M. Learning to model the tail. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 7032--7042. Google Scholar http://scholar.google.com/scholar_lookup?title=Wang Y X, Ramanan D, Hebert M. Learning to model the tail. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 7032--7042&

[38] Motiian S, Jones Q, Iranmanesh S M, et al. Few-shot adversarial domain adaptation. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 6673--6683. Google Scholar http://scholar.google.com/scholar_lookup?title=Motiian S, Jones Q, Iranmanesh S M, et al. Few-shot adversarial domain adaptation. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 6673--6683&

[39] Yu T H, Finn X, Xie A N, et al. One-shot imitation from observing humans via domain-adaptive meta-learning. 2018,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Yu T H, Finn X, Xie A N, et al. One-shot imitation from observing humans via domain-adaptive meta-learning. 2018,&

[40] Reed S E, Chen Y T, Paine T, et al. Few-shot autoregressive density estimation: towards learning to learn distributions. 2017,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Reed S E, Chen Y T, Paine T, et al. Few-shot autoregressive density estimation: towards learning to learn distributions. 2017,&

[41] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, Lille, 2015. 448--456. Google Scholar http://scholar.google.com/scholar_lookup?title=Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, Lille, 2015. 448--456&

[42] Kingma D P, Ba J. Adam: a method for stochastic optimization. 2014,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Kingma D P, Ba J. Adam: a method for stochastic optimization. 2014,&

[43] Wah C, Branson S, Welinder P, et al. The Caltech-UCSD Birds-200-2011 Dataset. Technical report, 2011. Google Scholar http://scholar.google.com/scholar_lookup?title=Wah C, Branson S, Welinder P, et al. The Caltech-UCSD Birds-200-2011 Dataset. Technical report, 2011&

[44] Mensink T, Verbeek J, Perronnin F. Distance-based image classification: generalizing to new classes at near-zero cost.. IEEE Trans Pattern Anal Mach Intell, 2013, 35: 2624-2637 CrossRef PubMed Google Scholar http://scholar.google.com/scholar_lookup?title=Distance-based image classification: generalizing to new classes at near-zero cost.&author=Mensink T&author=Verbeek J&author=Perronnin F&publication_year=2013&journal=IEEE Trans Pattern Anal Mach Intell&volume=35&pages=2624-2637

[45] Sung F, Yang Y X, Zhang L, et al. Learning to compare: relation network for few-shot learning. 2017,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Sung F, Yang Y X, Zhang L, et al. Learning to compare: relation network for few-shot learning. 2017,&