Fetching the paper…
Reading the bibliography…
Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning.
Zero-data learning of new tasks
H. Larochelle, D. Erhan, and Y. Bengio · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Zero-shot learning with semantic output codes
M. Palatucci, D. Pomerleau, G. E. Hinton, and T. M. Mitchell · 2009
Earlier work this paper cites.
Evaluating knowledge transfer and zero-shot learning in a large-scale setting
M. Rohrbach, M. Stark, and B. Schiele · 2011
Earlier work this paper cites.
Learning to share visual appearance for multiclass object detection
R. Salakhutdinov, A. Torralba, and J. Tenenbaum · 2011
Earlier work this paper cites.
Metric learning for large scale image classification: Generalizing to new classes at near-zero cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2012
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
Earlier work this paper cites.
Devise: A deep visual-semantic embedding model
A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, T. Mikolov, et al · 2013
Earlier work this paper cites.
Zero-shot learning through cross-modal transfer
R. Socher, M. Ganjoo, C. D. Manning, and A. Ng · 2013
Earlier work this paper cites.
Zero-shot learning by convex combination of semantic embeddings
M. Norouzi, T. Mikolov, S. Bengio, Y. Singer, J. Shlens, A. Frome, G. S. Corrado, and J. Dean · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. Manning · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Evaluation of output embeddings for fine-grained image classification
Z. Akata, S. Reed, D. Walter, H. Lee, and B. Schiele · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Cited alongside, same era.
An embarrassingly simple approach to zero-shot learning
B. Romera-Paredes and P. Torr · 2015
Cited alongside, same era.
Synthesized classifiers for zero-shot learning
S. Changpinyo, W.-L. Chao, B. Gong, and F. Sha · 2016
Cited alongside, same era.
An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
W.-L. Chao, S. Changpinyo, B. Gong, and F. Sha · 2016
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Later among the works it cites.
Semantic autoencoder for zero-shot learning
E. Kodirov, T. Xiang, and S. Gong · 2017
Later among the works it cites.
Zero-shot recognition using dual visual-semantic mapping paths
Y. Li, D. Wang, H. Hu, Y. Lin, and Y. Zhuang · 2017
Later among the works it cites.
From zero-shot learning to conventional supervised classification: Unseen visual data synthesis
Y. Long, L. Liu, L. Shao, F. Shen, G. Ding, and J. Han · 2017
Later among the works it cites.
From red wine to red tomato: Composition with context
I. Misra, A. Gupta, and M. Hebert · 2017
Later among the works it cites.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
Cited alongside, same era.
Unsupervised learning on neural network outputs: with application in zero-shot learning
Y. Lu · 2016
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
Cited alongside, same era.
Predicting visual exemplars of unseen classes for zero-shot learning
S. Changpinyo, W.-L. Chao, and F. Sha · 2017
Cited alongside, same era.
Zero shot learning via multi-scale manifold regularization
S. Deutsch, S. Kolouri, K. Kim, Y. Owechko, and S. Soatto · 2017
Cited alongside, same era.
Low-rank embedded ensemble semantic dictionary for zero-shot learning
Z. Ding, M. Shao, and Y. Fu · 2017
Cited alongside, same era.
Later among the works it cites.
Generalized zero-shot learning via synthesized examples
V. Kumar Verma, G. Arora, A. Mishra, and P. Rai · 2018
Closest in time.
Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X.-M. Wu · 2018
Closest in time.
Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2018
Closest in time.
Zero-shot recognition via semantic embeddings and knowledge graphs
X. Wang, Y. Ye, and A. Gupta · 2018
Closest in time.
Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly
Y. Xian, C. H. Lampert, B. Schiele, and Z. Akata · 2018
Closest in time.
H. Zhang and P. Koniusz · 2018
Closest in time.