C. H. Lampert, H. Nickisch, and S. Harmeling, Learning to detect unseen object classes by between-class attribute transfer, Proc. CVPR, pp.951-958, 2009.

H. Larochelle, D. Erhan, and Y. Bengio, Zero-data learning of new tasks, AAAI, vol.1, p.3, 2008.

M. Palatucci, D. Pomerleau, G. E. Hinton, and T. M. Mitchell, Zero-shot learning with semantic output codes, Proc. NIPS, pp.1410-1418, 2009.

Y. Xian, B. Schiele, and Z. Akata, Zero-shot learning-the good, the bad and the ugly, Proc. CVPR 2017, pp.3077-3086, 2017.

W. Chao, S. Changpinyo, B. Gong, and F. Sha, An empirical study and analysis of generalized zero-shot learning for object recognition in the wild, Proc. ECCV, pp.52-68, 2016.

C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, The CaltechUCSD Birds, 2011.

Y. Xian, C. H. Lampert, B. Schiele, and Z. Akata, Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly, 2017.

M. Bucher, S. Herbin, and F. Jurie, Generating visual representations for zero-shot classification, ICCV Workshops: TASK-CV, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01576222

V. K. Verma, G. Arora, A. Mishra, and P. Rai, Generalized zero-shot learning via synthesized examples, Proc. CVPR 2010, pp.4281-4289, 2018.

Y. Xian, T. Lorenz, B. Schiele, and Z. Akata, Feature generating networks for zero-shot learning, Proc. CVPR, 2018.

Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong, Transductive multi-view zero-shot learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.11, pp.2332-2345, 2015.

E. Kodirov, T. Xiang, Z. Fu, and S. Gong, Unsupervised domain adaptation for zero-shot learning, Proc. CVPR 2015, pp.2452-2460, 2015.

M. Rohrbach, S. Ebert, and B. Schiele, Transfer learning in a transductive setting, Proc. NIPS 2013, pp.46-54, 2013.

C. M. Bishop, Pattern Recognition and Machine Learning, 2006.

W. N. Van-wieringen, Lecture notes on ridge regression, 2015.

Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid, Label-embedding for image classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.7, pp.1425-1438, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01207145

A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean et al., Devise: A deep visual-semantic embedding model, Proc. NIPS 2013, pp.2121-2129, 2013.

Z. Akata, S. Reed, D. Walter, H. Lee, and B. Schiele, Evaluation of output embeddings for fine-grained image classification, Proc. CVPR, pp.2927-2936, 2015.

S. Changpinyo, W. Chao, B. Gong, and F. Sha, Synthesized classifiers for zero-shot learning, Proc. CVPR 2016, pp.5327-5336, 2016.

B. Romera-paredes and P. Torr, An embarrassingly simple approach to zero-shot learning, Proc. ICML 2015, pp.2152-2161, 2015.

E. Kodirov, T. Xiang, and S. Gong, Semantic autoencoder for zero-shot learning, Proc. CVPR 2017, pp.4447-4456, 2017.

Y. Shigeto, I. Suzuki, K. Hara, M. Shimbo, and Y. Matsumoto, Ridge regression, hubness, and zero-shot learning, Proc. ECML PKDD, pp.135-151, 2015.

M. Radovanovi´cradovanovi´c, A. Nanopoulos, and M. Ivanovi´civanovi´c, Hubs in space: Popular nearest neighbors in high-dimensional data, Journal of Machine Learning Research, vol.11, pp.2487-2531, 2010.

C. H. Lampert, H. Nickisch, and S. Harmeling, Attribute-based classification for zero-shot visual object categorization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.3, pp.453-465, 2014.

G. Patterson and J. Hays, Sun attribute database: Discovering, annotating, and recognizing scene attributes, Proc. CVPR 2012, pp.2751-2758, 2012.

A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth, Describing objects by their attributes, CVPR 2009. IEEE Conference on, p. Proc. CVPR, 2009.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., Imagenet: A large-scale hierarchical image database, Proc. CVPR, pp.248-255, 2009.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, Proc. CVPR, pp.1-9, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proc. CVPR 2016, pp.770-778, 2016.

, 529 for ALE and SJE) but the lowest harmonic mean score with 55.0 (per class accuracy, compared to respectively 56.9 and 59.4 for ALE and SJE)

, evaluated with per class (p.c.) and per sample (p.s.) accuracies. With calibration and ? * GZSL, Table 8: ZSL and GZSL scores with 10-crop features