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In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.
Automated flower classification over a large number of classes
Nilsback, M.E., Zisserman, A.: · 2008
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Describing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: · 2009
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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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Learning to detect unseen object classes by between-class attribute transfer
Lampert, C.H., Nickisch, H., Harmeling, S.: · 2009
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Caltech-ucsd birds 200
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, V., Hinton, G.E.: · 2010
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Zero-shot learning through cross-modal transfer
Socher, R., Ganjoo, M., Manning, C.D., Ng, A.: · 2013
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Designing category-level attributes for discriminative visual recognition
Yu, F.X., Cao, L., Feris, R.S., Smith, J.R., Chang, S.F.: · 2013
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Devise: A deep visual-semantic embedding model
Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Mikolov, T., et al.: · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, A.L., Hannun, A.Y., Ng, A.Y.: · 2013
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Attribute-based classification for zero-shot visual object categorization
Lampert, C.H., Nickisch, H., Harmeling, S.: · 2014
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Zero-shot learning by convex combination of semantic embeddings
Norouzi, M., Mikolov, T., Bengio, S., Singer, Y., Shlens, J., Frome, A., Corrado, G., Dean, J.: · 2014
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Zero-shot learning via semantic similarity embedding
Zhang, Z., Saligrama, V.: · 2015
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Evaluation of output embeddings for fine-grained image classification
Akata, Z., Reed, S., Walter, D., Lee, H., Schiele, B.: · 2015
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An embarrassingly simple approach to zero-shot learning
Romera-Paredes, B., Torr, P.: · 2015
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Less is more: zero-shot learning from online textual documents with noise suppression
Qiao, R., Liu, L., Shen, C., van den Hengel, A.: · 2016
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: · 2016
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Zero-shot learning – the Good, the Bad and the Ugly
Xian, Y., Schiele, B., Akata, Z.: · 2017
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Zero-shot learning using synthesised unseen visual data with diffusion regularisation
Long, Y., Liu, L., Shen, F., Shao, L., Li, X.: · 2017
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Generating visual representations for zero-shot classification
M. Bucher, S. Herbin, F.J.: · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: · 2017
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A bayesian data augmentation approach for learning deep models
Tran, T., Pham, T., Carneiro, G., Palmer, L., Reid, I.: · 2017
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Label-embedding for image classification
Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: · 2016
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Latent embeddings for zero-shot classification
Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., Schiele, B.: · 2016
Cited alongside, same era.
Synthesized classifiers for zero-shot learning
Changpinyo, S., Chao, W.L., Gong, B., Sha, F.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Attribute2image: Conditional image generation from visual attributes
Yan, X., Yang, J., Sohn, K., Lee, H.: · 2016
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Learning deep representations of fine-grained visual descriptions
Reed, S., Akata, Z., Lee, H., Schiele, B.: · 2016
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Semantic Autoencoder for Zero-shot Learning
Elyor Kodirov, T.X., Gong, S.: · 2017
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Wasserstein gan
Arjovsky, M., Chintala, S., Bottou, L.: · 2017
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Multi-attention network for one shot learning
Wang, P., Liu, L., Shen, C., Huang, Z., van den Hengel, A., Shen, H.T.: · 2017
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Feature generating networks for zero-shot learning
Xian, Y., Lorenz, T., Schiele, B., Akata, Z.: · 2018
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Zero-shot visual recognition using semantics-preserving adversarial embedding networks
Chen, L., Zhang, H., Xiao, J., Liu, W., Chang, S.F.: · 2018
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Preserving semantic relations for zero-shot learning
Annadani, Y., Biswas, S.: · 2018
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