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Zero-shot learning (ZSL) is a framework to classify images belonging to unseen classes based on solely semantic information about these unseen classes.
Pac nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces
Ciaccia, P. and Patella, M. (2000) · 2000
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Learning with local and global consistency
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., and Schölkopf, B. (2003) · 2003
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Least angle regression
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R., et al. (2004) · 2004
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The use of entropy minimization for the solution of blind source separation problems in image analysis
Guo, L. and Garland, M. (2006) · 2006
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Sequential unsupervised domain adaptation through prototypical distributions
Rostami, M. and Galstyan, A. (2020) · 2007
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Attribute and simile classifiers for face verification
Kumar, N., Berg, A. C., Belhumeur, P. N., and Nayar, S. K. (2009) · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., and Harmeling, S. (2009) · 2009
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A unified framework for high-dimensional analysis of m m -estimators with decomposable regularizers
Negahban, S., Yu, B., Wainwright, M., and Ravikumar, P. (2009) · 2009
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Zero-shot learning with semantic output codes
Palatucci, M., Pomerleau, D., Hinton, G. E., and Mitchell, T. M. (2009) · 2009
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Image super-resolution via sparse representation
Yang, J., Wright, J., Huang, T. S., and Ma, Y. (2010) · 2010
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Boyd, S., Parikh, N., and Chu, E. (2011) · 2011
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Domain adaptation via transfer component analysis
Pan, S., Tsang, I. W., Kwok, J. T., and Yang, Q. (2011) · 2011
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. (2011) · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Ssim-inspired image restoration using sparse representation
Rehman, A., Rostami, M., Wang, Z., Brunet, D., and Vrscay, E. R. (2012) · 2012
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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) · 2013
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Attribute-based classification for zero-shot visual object categorization
Lampert, C. H., Nickisch, H., and Harmeling, S. (2013) · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., 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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Improving zero-shot learning by mitigating the hubness problem
Dinu, G., Lazaridou, A., and Baroni, M. (2014) · 2014
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Attribute-based classification for zero-shot visual object categorization
Lampert, C. H., Nickisch, H., and Harmeling, S. (2014) · 2014
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Costa: Co-occurrence statistics for zero-shot classification
Mensink, T., Gavves, E., and Snoek, C. G. M. (2014) · 2014
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Properties of artificial networks evolved to contend with natural spectra
Morgenstern, Y., Rostami, M., and Purves, D. (2014) · 2014
Cited alongside, same era.
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. (2014) · 2014
Cited alongside, same era.
Proximal algorithms
Parikh, N., Boyd, S., et al. (2014) · 2014
Cited alongside, same era.
The sun attribute database: Beyond categories for deeper scene understanding
Patterson, G., Xu, C., Su, H., and Hays, J. (2014) · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
Cited alongside, same era.
Evaluation of output embeddings for fine-grained image classification
Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K. Q., and van der Maaten, L. (2017) · 2017
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Semantic autoencoder for zero-shot learning
Kodirov, E., Xiang, T., and Gong, S. (2017) · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R. (2017) · 2017
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Zero-shot visual recognition via bidirectional latent embedding
Wang, Q. and Chen, K. (2017) · 2017
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Matrix tri-factorization with manifold regularizations for zero-shot learning
Xu, X., Shen, F., Yang, Y., Zhang, D., Shen, H. T., and Song, J. (2017) · 2017
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Zero-shot classification with discriminative semantic representation learning
Ye, M. and Guo, Y. (2017) · 2017
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Akata, Z., Reed, S., Walter, D., Lee, H., and Schiele, B. (2015) · 2015
Cited alongside, same era.
Transductive multi-view zero-shot learning
Fu, Y., Hospedales, T. M., Xiang, T., and Gong, S. (2015) · 2015
Cited alongside, same era.
Sample complexity of dictionary learning and other matrix factorizations
Gribonval, R., Jenatton, R., Bach, F., Kleinsteuber, M., and Seibert, M. (2015) · 2015
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
Cited alongside, same era.
Generalized zero-shot learning with deep calibration network
Liu, S., Long, M., Wang, J., and Jordan, M. I. (2018) · 2015
Cited alongside, same era.
An embarrassingly simple approach to zero-shot learning
Romera-Paredes, B. and Torr, P. (2015) · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015) · 2015
Cited alongside, same era.
Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S. (2017) · 2017
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Lifelong machine learning
Chen, Z. and Liu, B. (2018) · 2018
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Generative zero-shot learning via low-rank embedded semantic dictionary
Ding, Z., Shao, M., and Fu, Y. (2018) · 2018
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Joint dictionaries for zero-shot learning
Kolouri, S., Rostami, M., Owechko, Y., and Kim, K. (2018) · 2018
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Attribute-based synthetic network (abs-net): Learning more from pseudo feature representations
Lu, J., Li, J., Yan, Z., Mei, F., and Zhang, C. (2018) · 2018
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Unsupervised domain adaptation with similarity learning
Pinheiro, P. O. (2018) · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., and Harada, T. (2018) · 2018
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Learning to compare: Relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., and Hospedales, T. M. (2018) · 2018
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A. (2018) · 2018
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Zero-shot image recognition using relational matching, adaptation and calibration
Das, D. and Lee, C. (2019) · 2019
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Leveraging the invariant side of generative zero-shot learning
Li, J., Jing, M., Lu, K., Ding, Z., Zhu, L., and Huang, Z. (2019) · 2019
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Meta-transfer learning for few-shot learning
Sun, Q., Liu, Y., Chua, T.-S., and Schiele, B. (2019) · 2019
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Avalanche: an end-to-end library for continual learning
Lomonaco, V., Pellegrini, L., Cossu, A., Carta, A., Graffieti, G., Hayes, T. L., De Lange, M., Masana, M., Pomponi, J., van de Ven, G. M., et al. (2021) · 2021
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Detection and continual learning of novel face presentation attacks
Rostami, M., Spinoulas, L., Hussein, M., Mathai, J., and Abd-Almageed, W. (2021) · 2021
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Unsupervised model adaptation for continual semantic segmentation
Stan, S. and Rostami, M. (2021) · 2021
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Low-rank embedded ensemble semantic dictionary for zero-shot learning
Ding, Z., S., M., and Fu, Y. (2017) · 2058
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