[1] Schölkopf B, Smola A J. Learning with Kernels. Cambridge: MIT Press, 2001.
[2] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems. Lake Tahoe, USA: MIT Press, 2012. 1097-1105
[3] Gregor K, Danihelka I, Graves A, Rezende D J, Wierstra D. DRAW: a recurrent neural network for image generation. arXiv preprint arXiv: 1502.04623, 2015. http://cn.bing.com/academic/profile?id=046d1bffdbfb5068f065d8bdf9403628&encoded=0&v=paper_preview&mkt=zh-cn
[4] Biederman I. Recognition-by-components: a theory of human image understanding. Psychological Review, 1987, 94(2): 115-147 doi: 10.1037/0033-295X.94.2.115
[5] Yao B P, Khosla A, Li F F. Combining randomization and discrimination for fine-grained image categorization. In: Proceedings of the Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE, 2011. 1577-1584
[6] Murphy G L. The Big Book of Concepts. Cambridge: MIT Press, 2004. https://mitpress.mit.edu/books/big-book-concepts
[7] Koggalage R, Halgamuge S K. Reducing the number of training samples for fast support vector machine classification. Neural Information Processing, 2004, 2(3): 57-65 http://cn.bing.com/academic/profile?id=d54b53441cc53c03e26037d670f4bc72&encoded=0&v=paper_preview&mkt=zh-cn
[8] Li F F, Fergus R, Perona P. One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 594-611 doi: 10.1109/TPAMI.2006.79
[9] Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap T. One-shot learning with memory-augmented neural networks. arXiv preprint arXiv: 1605.06065, 2016.
[10] Fanello S R, Gori I, Metta G, Odone F. One-shot learning for real-time action recognition. In: Proceedings of Pattern Recognition and Image Analysis. Berlin, Heidelberg: Springer, 2013. 31-40
[11] Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359 doi: 10.1109/TKDE.2009.191
[12] Bakker B, Heskes T. Task clustering and gating for Bayesian multitask learning. Journal of Machine Learning Research, 2003, 4(12): 83-99 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=065e5313fe59658a6e44688df00aff1b
[13] Bonilla E V, Agakov F V, Williams C K I. Kernel multi-task learning using task-specific features. In: Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, Atherton, USA: PMLR, 2007. 43-50
[14] Lampert C H, Nickisch H, Harmeling S. Learning to detect unseen object classes by between-class attribute transfer. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009. 951-958
[15] Palatucci M, Pomerleau D, Hinton G, Mitchell T M. Zero-shot learning with semantic output codes. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates Inc., 2009. 1410-1418
[16] Ba J L, Swersky K, Fidler S, Salakhutdinov R. Predicting deep zero-shot convolutional neural networks using textual descriptions. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 4247-4255
[17] Zhang L, Xiang T, Gong S G. Learning a deep embedding model for zero-shot learning. arXiv preprint arXiv: 1611.05088, 2016.
[18] Zhang D, Liu Y, Si L. Serendipitous learning: learning beyond the predefined label space. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, USA: ACM, 2011. 1343-1351 http://cn.bing.com/academic/profile?id=a5c71403e19916c25cb401816c1c4b16&encoded=0&v=paper_preview&mkt=zh-cn
[19] Du C, Zhuang F, He J, He Q, Long G. Learning beyond predefined label space via bayesian nonparametric topic modelling. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham, Riva del Garda, Italy: Springer, 2016. 148-164
[20] Zhuang F Z, Luo P, Shen Z Y, He Q, Xiong Y H, Shi Z Z. D-LDA: a topic modeling approach without constraint generation for semi-defined classification. In: Proceedings of the 2010 IEEE International Conference on Data Mining. Sydney, Australia: IEEE, 2010. 709-718
[21] Larochelle H, Erhan D, Bengio Y. Zero-data learning of new tasks. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence. Chicago, USA: AAAI, 2013. 646-651
[22] Socher R, Ganjoo M, Sridhar H, Bastani O, Manning C D, Ng A Y. Zero-shot learning through cross-modal transfer. In: Proceedings of the Advances in Neural Information Processing Systems. Lake Tahoe, USA: MIT Press, 2013. 935-943
[23] Romera-Paredes B, Torr P H S. An embarrassingly simple approach to zero-shot learning. In: Proceedings of the 32nd International Conference on Machine Learning. Lille, France: ACM, 2015. 2152-2161
[24] Qiao R Z, Liu L Q, Shen C H, van den Hengel A. Less is more: zero-shot learning from online textual documents with noise suppression. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 2249-2257
[25] Dietterich T G, Bakiri G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 1994, 2: 263-286 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_cs%2f9501101
[26] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. New York: Springer, 2001.
[27] Sloman S A. Feature-based induction. Cognitive Psychology, 1993, 25(2): 231-280 doi: 10.1006/cogp.1993.1006
[28] Osherson D, Smith E E, Myers T S, Shafir E, Stob M. Extrapolating human probability judgment. Theory & Decision, 1994, 36(2): 103-129 http://cn.bing.com/academic/profile?id=1acd40713768044e31d5634955b76578&encoded=0&v=paper_preview&mkt=zh-cn
[29] Ferrari V, Zisserman A. Learning visual attributes. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates Inc., 2007. 433-440
[30] van de Weijer J, Schmid C, Verbeek J. Learning color names from real-world images. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA: IEEE, 2007. 1-8
[31] Yanai K, Barnard K. Image region entropy: a measure of "visualness" of web images associated with one concept. In: Proceedings of the 13th Annual ACM International Conference on Multimedia. New York, USA: ACM, 2005. 419-422
[32] Farhadi A, Endres I, Hoiem D, Forsyth D. Describing objects by their attributes. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR). 2009. Miami Beach, USA: IEEE, 2009. 1778-1785
[33] Lampert C H, Nickisch H, Harmeling S. Attribute-based classification for zero-shot visual object categorization. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 36(3): 453-465 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=485cb3a8f8e0a8984bad0fe6069ea2d9
[34] Suzuki M, Sato H, Oyama S, Kurihara M. Transfer learning based on the observation probability of each attribute. In: Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics. San Diego, USA: IEEE, 2014. 3627-3631
[35] Kovashka A, Parikh D, Grauman K. WhittleSearch: image search with relative attribute feedback. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012. 2973-2980
[36] Parkash A, Parikh D. Attributes for classifier feedback. In: Proceedings of the European Conference on Computer Vision. Berlin, Heidelberg: Springer, 2012. 354-368
[37] Kulkarni G, Premraj V, Dhar S, Li S M, Choi Y J, Berg A C, et al. Baby talk: understanding and generating simple image descriptions. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE, 2011. 1601-1608
[38] Kumar N, Berg A, Belhumeur P N, Nayar S. Describable visual attributes for face verification and image search. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(10): 1962-1977 doi: 10.1109-TPAMI.2011.48/
[39] Liu J, Kuipers B, Savarese S. Recognizing human actions by attributes. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE, 2011. 3337-3344
[40] Patterson G, Hays J. SUN attribute database: discovering, annotating, and recognizing scene attributes. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012. 2751-2758
[41] Feris R, Siddiquie B, Zhai Y, Petterson J, Brown L, Pankanti S. Attribute-based vehicle search in crowded surveillance videos. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval. Trento, Italy: ACM, 2011. Article No. 18
[42] Fu Y W, Hospedales T M, Xiang T, Fu Z Y, Gong S G. Transductive multi-view embedding for zero-shot recognition and annotation. In: Proceedings of European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 584-599
[43] Chao W L, Changpinyo S, Gong B, Sha F. An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In: Proceedings of European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 52-68
[44] Lazaridou A, Dinu G, Baroni M. Hubness and pollution: delving into cross-space mapping for zero-shot learning. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing, China: ACL, 2015. 270-280
[45] Norouzi M, Mikolov T, Bengio S, Singer Y, Shlens J, Frome A, et al. Zero-shot learning by convex combination of semantic embeddings. arXiv preprint arXiv: 1312.5650, 2013
[46] Changpinyo S, Chao W L, Gong B Q, Sha F. Synthesized classifiers for zero-shot learning. In: Proceedings of the 2016 IEEE Conference on Computer vision and pattern recognition. Las Vegas, USA: IEEE, 2016. 5327-5336
[47] Radovanović M, Nanopoulos A, Ivanović M. Hubs in space: popular nearest neighbors in high-dimensional data. Journal of Machine Learning Research, 2010, 11: 2487-2531 http://cn.bing.com/academic/profile?id=cd255ab6835ff4b1191724b737470cd3&encoded=0&v=paper_preview&mkt=zh-cn
[48] Radovanović M, Nanopoulos A, Ivanović M. On the existence of obstinate results in vector space models. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Geneva, Switzerland: ACM, 2010. 186-193
[49] Dinu G, Lazaridou A, Baroni M. Improving zero-shot learning by mitigating the hubness problem. arXiv preprint arXiv: 1412.6568, 2014
[50] Kodirov E, Xiang T, Fu Z Y, Gong S G. Unsupervised domain adaptation for zero-shot learning. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 2452-2460
[51] Harris Z S. Distributional structure. Word, 1954, 10(2-3): 146-162 doi: 10.1080/00437956.1954.11659520
[52] Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of Advances in Neural Information Processing Systems. Lake Tahoe, USA: MIT Press, 2013. 3111-3119
[53] Pennington J, Socher R, Manning C D. GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: ACL, 2014. 1532-1543 http://cn.bing.com/academic/profile?id=537a8511c9ae20478a13d76f2a1e7035&encoded=0&v=paper_preview&mkt=zh-cn
[54] Blanchard E, Harzallah M, Briand H, Kuntz P. A typology of ontology-based semantic measures. In: EMOI-INTEROP. Portugal: Springer, 2005. 160
[55] Frome A, Corrado G S, Shlens J, Bengio S, Dean J, Ranzato M, et al. DeViSE: a deep visual-semantic embedding model. In: Proceedings of Advances in Neural Information Processing Systems. Lake Tahoe, USA: MIT Press, 2013. 2121-2129
[56] Akata Z, Perronnin F, Harchaoui Z, Schmid C. Label-embedding for attribute-based classification. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE, 2013. 819-826
[57] Akata Z, Reed S, Walter D, Lee H, Schiele B. Evaluation of output embeddings for fine-grained image classification. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA: IEEE, 2015. 2927-2936
[58] Reed S, Akata Z, Lee H, Schiele B. Learning deep representations of fine-grained visual descriptions. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 49-58
[59] Kodirov E, Xiang T, Gong S G. Semantic autoencoder for zero-shot learning. arXiv preprint arXiv: 1704.08345, 2017
[60] Bucher M, Herbin S, Jurie F. Improving semantic embedding consistency by metric learning for zero-shot classiffication. In: Proceedings of European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 730-746
[61] Zhang Z M, Saligrama V. Zero-shot learning via joint latent similarity embedding. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 6034-6042
[62] Xian Y Q, Akata Z, Sharma G, Nguyen Q, Hein M, Schiele B. Latent embeddings for zero-shot classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 69-77
[63] Jayaraman D, Grauman K. Zero-shot recognition with unreliable attributes. In: Proceedings of the International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press, 2014. 3464-3472
[64] Zhang Z M, Saligrama V. Zero-shot learning via semantic similarity embedding. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE 2015. 4166-4174
[65] Zhao B, Wu B T, Wu T F, Wang Y Z. Zero-shot learning posed as a missing data problem. arXiv preprint arXiv: 1612.00560, 2016
[66] Zhang Z M, Saligrama V. Zero-shot recognition via structured prediction. In: European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 533-548
[67] Wang D H, Li Y, Lin Y T, Zhuang Y T. Relational knowledge transfer for zero-shot learning. In: Proceedings of the 13th AAAI Conference on Artificial Intelligence. Phoenix, USA: AAAI, 2016. 2145-2151
[68] Luo C Z, Li Z T, Huang K Z, Feng J S, Wang M. Zero-shot learning via attribute regression and class prototype rectification. IEEE Transactions on Image Processing, 2018, 27(2): 637-648 http://cn.bing.com/academic/profile?id=cc3cb532b2789d33ffe0a8326dbeeec3&encoded=0&v=paper_preview&mkt=zh-cn
[69] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. In: Advances in Neural Information Processing Systems 27. Montreal, Canada: MIT Press, 2014. 1-9
[70] Lu Y. Unsupervised learning on neural network outputs: with application in zero-shot learning. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. New York, USA: AAAI, 2016. 3432-3438
[71] Baldi P, Hornik K. Neural networks and principal component analysis: learning from examples without local minima. Neural Networks, 1989, 2(1): 53-58 http://cn.bing.com/academic/profile?id=71193a90089a19100017f3263a421ddb&encoded=0&v=paper_preview&mkt=zh-cn
[72] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 1999, 10(3): 626-634 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=df2ada21711f3db3a676da39586b8210
[73] Deng J, Dong W, Socher R, Li L J, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami Beach, USA: IEEE, 2009. 248-255
[74] Song J, Shen C C, Yang Y Z, Liu Y, Song M L. Transductive unbiased embedding for zero-shot learning. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 1024-1033
[75] Guo Y C, Ding G G, Jin X M, Wang J M. Transductive zero-shot recognition via shared model space learning. In: Proceedings of the 13th AAAI Conference on Artificial Intelligence. Phoenix, USA: AAAI Press, 2016. 3434-3500
[76] Xian Y Q, Lampert C H, Schiele B, Akata Z. Zero-shot learning-A comprehensive evaluation of the good, the bad and the ugly. arXiv preprint arXiv: 1707.00600, 2017
[77] Wah C, Branson S, Welinder P, Perona P, Belongie S. The caltech-UCSD birds-200-2011 dataset, Computation & Neural Systems Technical Report, California Institute of Technology, Pasadena, CA, 2011. https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf
[78] Xiao J X, Hays J, Ehinger K A, Oliva A, Torralba A. Sun database: large-scale scene recognition from abbey to zoo. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 3485-3492
[79] Antol S, Zitnick C L, Parikh D. Zero-shot learning via visual abstraction. In: Proceedings of the European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 401-416
[80] Robyns P, Marin E, Lamotte W, Quax P, Singelée D, Preneel B. Physical-layer fingerprinting of LoRa devices using supervised and zero-shot learning. In: Proceedings of the 10th ACM Conference on Security and Privacy in Wireless and Mobile Networks. Boston, Massachusetts: ACM, 2017. 58-63
[81] Yang Y, Luo Y D, Chen W L, Shen F M, Shao J, Shen H T. Zero-shot hashing via transferring supervised knowledge. In: Proceedings of the 24th ACM International Conference on Multimedia. Amsterdam, The Netherlands: ACM, 2016. 1286-1295
[82] Johnson M, Schuster M, Le Q V, Krikun M, Wu Y H, Chen Z F, et al. Google's multilingual neural machine translation system: enabling zero-shot translation. arXiv preprint arXiv: 1611.04558, 2016.
[83] Veeranna S P, Nam J, Mencía E L, Furnkranz J. Using semantic similarity for multi-label zero-shot classification of text documents. In: Proceeding of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: Elsevier, 2016. 423-428