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Learning to classify unseen class samples at test time is popularly referred to as zero-shot learning (ZSL).
Figr: Few-shot image generation with reptile
Clouâtre, L. and Demers, M. (2019) · 1901
Earlier work this paper cites.
A generative framework for zero-shot learning with adversarial domain adaptation
Khare, V., Mahajan, D., Bharadhwaj, H., Verma, V., and Rai, P. (2019) · 1906
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Generalized zero shot learning with deep calibration network
Liu, S., Long, M., Wang, and Jordan, M. I. (2018) · 2006
Earlier work this paper cites.
Describing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., and Forsyth, D. (2009) · 2009
Earlier work this paper cites.
Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., and Harmeling, S. (2009) · 2009
Earlier work this paper cites.
Caltech-ucsd birds 200
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., and Perona, P. (2010) · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A. (2010) · 2010
Earlier work this paper cites.
Label-embedding for attribute-based classification
Akata, Z., Perronnin, F., Harchaoui, Z., and Schmid, C. (2013) · 2013
Earlier work this paper cites.
Devise: A deep visual-semantic embedding model
Frome, A., Corrado, G. S., Shlens, J., Bengio, S., Dean, J., Mikolov, T., et al. (2013) · 2013
Earlier work this paper cites.
Zero-shot learning by convex combination of semantic embeddings
Norouzi, M., Mikolov, T., Bengio, S., Singer, Y., Shlens, J., Frome, A., Corrado, G. S., and Dean, J. (2013) · 2013
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Zero-shot learning through cross-modal transfer
Socher, R., Ganjoo, M., Manning, C. D., and Ng, A. (2013) · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
Earlier work this paper cites.
Attribute-based classification for zero-shot visual object categorization
Lampert, C. H., Nickisch, H., and Harmeling, S. (2014) · 2014
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Evaluation of output embeddings for fine-grained image classification
Akata, Z., Reed, S., Walter, D., Lee, H., and Schiele (2015) · 2015
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Exploring semantic inter-class relationships (sir) for zero-shot action recognition
Gan, C., Lin, M., Yang, Y., Zhuang, Y., and Hauptmann, A. G. (2015) · 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., et al. (2015) · 2015
Earlier work this paper cites.
Semantic embedding space for zero-shot action recognition
Xu, X., Hospedales, T., and Gong, S. (2015) · 2015
Earlier work this paper cites.
Zero-shot learning via semantic similarity embedding
Zhang, Z. and Saligrama, V. (2015) · 2015
Earlier work this paper cites.
Synthesized classifiers for zero-shot learning
Changpinyo, S., Chao, W.-L., Gong, B., and Sha, F. (2016) · 2016
Cited alongside, same era.
An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
Chao, W.-L., Changpinyo, S., Gong, B., and Sha, F. (2016) · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H. (2016) · 2016
Cited alongside, same era.
Learning deep representations of fine-grained visual descriptions
Reed, S., Akata, Z., Lee, H., and S, B. (2016) · 2016
Cited alongside, same era.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al. (2016) · 2016
Cited alongside, same era.
Latent embeddings for zero-shot classification
Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., and Schiele, B. (2016) · 2016
Zero-shot visual recognition using semantics-preserving adversarial embedding networks
Chen, L., Zhang, H., Xiao, J., Liu, W., and Chang, S.-F. (2018) · 2018
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Multi-modal cycle-consistent generalized zero-shot learning
Felix, R., Vijay Kumar, B., Reid, I., and Carneiro, G. (2018) · 2018
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The variational homoencoder: Learning to learn high capacity generative models from few examples
Hewitt, L. B., Nye, M. I., Gane, A., Jaakkola, T., and Tenenbaum, J. B. (2018) · 2018
Later among the works it cites.
Correction networks: Meta-learning for zero-shot learning
Hu, R. L., Xiong, C., and Socher, R. (2018) · 2018
Later among the works it cites.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and L, J. (2018) · 2018
Later among the works it cites.
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Cited alongside, same era.
Learning joint feature adaptation for zero-shot recognition
Zhang, Z. and Saligrama, V. (2016) · 2016
Cited alongside, same era.
Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
Cited alongside, same era.
Generating visual representations for zero-shot classification
Bucher, M., Herbin, S., and Jurie, F. (2017) · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S. (2017) · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C. (2017) · 2017
Cited alongside, same era.
Synthesizing samples for zero-shot learning
Guo, Y., Ding, G., Han, J., and Gao, Y. (2017) · 2017
Cited alongside, same era.
A generative approach to zero-shot and few-shot action recognition
Mishra, A., Verma, V. K., Reddy, M., Rai, P., and Mittal, A. (2018) · 2018
Later among the works it cites.
Zero-shot sketch-image hashing
Shen, Y., Liu, L., Shen, F., and Shao, L. (2018) · 2018
Later among the works it cites.
Transductive unbiased embedding for zero-shot learning
Song, J., Shen, C., Yang, Y., Liu, Y., and S, M. (2018) · 2018
Later among the works it cites.
Generalized zero-shot learning via synthesized examples
Verma, V. K., Arora, G., Mishra, A., and Rai, P. (2018) · 2018
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Zero-shot learning via class-conditioned deep generative models
Wang, W., Pu, Y., Verma, V. K., Fan, K., Zhang, Y., Chen, C., Rai, P., and Carin, L. (2018) · 2018
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Zero-shot kernel learning
Zhang, H. and Koniusz, P. (2018) · 2018
Later among the works it cites.
A generative adversarial approach for zero-shot learning from noisy texts
Zhu, Y., Elhoseiny, M., Liu, B., Peng, X., and Elgammal, A. (2018) · 2018
Later among the works it cites.
Generative model for zero-shot sketch-based image retrieval
Kumar Verma, V., Mishra, A., Mishra, A., and Rai, P. (2019) · 2019
Closest in time.
Leveraging the invariant side of generative zero-shot learning
Li, J., Jin, M., Lu, K., Ding, Z., Zhu, L., and Huang, Z. (2019) · 2019
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Out-of-distribution detection for generalized zero-shot action recognition
Mandal, D., Narayan, S., Dwivedi, S. K., Gupta, V., Ahmed, S., Khan, F. S., and Shao, L. (2019) · 2019
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Zero-shot knowledge distillation in deep networks
Nayak, G. K., M. K. R. S. V. B. R. V. and Chakraborty, A. (2019) · 2019
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Generalized zero-and few-shot learning via aligned variational autoencoders
Schonfeld, E., Ebrahimi, S., Sinha, S., Darrell, T., and Akata, Z. (2019) · 2019
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f-vaegan-d2: A feature generating framework for any-shot learning
Xian, Y., Sharma, S., Schiele, B., and Akata, Z. (2019) · 2019
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Generalized zero-shot recognition based on visually semantic embedding
Zhu, Pengkai, Wang, H., and Saligrama, V. (2019) · 2019
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