Fetching the paper…
Reading the bibliography…
Effective convolutional neural networks are trained on large sets of labeled data.
B. B. Le Cun, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Handwritten digit recognition with a back-propagation network,” in Advances in neural information processing systems
1990
Earlier work this paper cites.
V. R. de Sa, “Learning classification with unlabeled data,” in Advances in neural information processing systems
1994
Earlier work this paper cites.
D. J. Miller and H. S. Uyar, “A mixture of experts classifier with learning based on both labelled and unlabelled data,” in Advances in neural information processing systems
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
A. Blum and T. Mitchell, “Combining labeled and unlabeled data with co-training,” in Proceedings of the eleventh annual conference on Computational learning theory
1998
Earlier work this paper cites.
T. Joachims, “Transductive inference for text classification using support vector machines,” in ICML
1999
Earlier work this paper cites.
K. Bennett, A. Demiriz, et al
1999
Earlier work this paper cites.
A. Blum and S. Chawla, “Learning from labeled and unlabeled data using graph mincuts,” 2001
2001
Earlier work this paper cites.
X. Zhu and Z. Ghahramani, “Learning from labeled and unlabeled data with label propagation,” tech. rep., Citeseer, 2002
2002
Earlier work this paper cites.
X. Zhu, Z. Ghahramani, J. Lafferty, et al
2003
Earlier work this paper cites.
Y. LeCun, F. J. Huang, and L. Bottou, “Learning methods for generic object recognition with invariance to pose and lighting,” in Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
2004
Earlier work this paper cites.
X. Zhu, “Semi-supervised learning literature survey,” 2005
2005
Earlier work this paper cites.
O. Chapelle, B. Schölkopf, A. Zien, et al
2006
Earlier work this paper cites.
K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun, “What is the best multi-stage architecture for object recognition?,” in Computer Vision, 2009 IEEE 12th International Conference on
2009
Cited alongside, same era.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” 2009
2009
Cited alongside, same era.
A. Berg, J. Deng, and L. Fei-Fei, “Large scale visual recognition challenge 2010,” 2010
2010
Cited alongside, same era.
Y. LeCun, K. Kavukcuoglu, C. Farabet, et al
2010
Cited alongside, same era.
2010
Cited alongside, same era.
A. Krizhevskey, “Cuda-convnet.” code.google.com/p/cuda-convnet , 2014
2014
Later among the works it cites.
B. Graham, “Spatially-sparse convolutional neural networks,” arXiv preprint arXiv:1409.6070
2014
Later among the works it cites.
B. Graham, “Fractional max-pooling,” arXiv preprint arXiv:1412.6071
2014
Later among the works it cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Later among the works it cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in International conference on artificial intelligence and statistics
2010
Cited alongside, same era.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in NIPS workshop on deep learning and unsupervised feature learning
2011
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems
2012
Cited alongside, same era.
2012
Cited alongside, same era.
D. Ciresan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
2012
Cited alongside, same era.
J. Weston, F. Ratle, H. Mobahi, and R. Collobert, “Deep learning via semi-supervised embedding,” in Neural Networks: Tricks of the Trade
2012
Cited alongside, same era.
2013
Cited alongside, same era.
2015
Later among the works it cites.
P. Agrawal, J. Carreira, and J. Malik, “Learning to see by moving,” in Proceedings of the IEEE International Conference on Computer Vision
2015
Later among the works it cites.
C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in Proceedings of the IEEE International Conference on Computer Vision
2015
Later among the works it cites.
R. Johnson and T. Zhang, “Semi-supervised convolutional neural networks for text categorization via region embedding,” in Advances in Neural Information Processing Systems
2015
Later among the works it cites.
X. Wang and A. Gupta, “Unsupervised learning of visual representations using videos,” in Proceedings of the IEEE International Conference on Computer Vision
2015
Later among the works it cites.
D. Jayaraman and K. Grauman, “Learning image representations tied to ego-motion,” in Proceedings of the IEEE International Conference on Computer Vision
2015
Later among the works it cites.
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, “Semi-supervised learning with ladder networks,” in Advances in Neural Information Processing Systems
2015
Later among the works it cites.
2015
Later among the works it cites.
M. Sajjadi, M. Javanmardi, and T. Tasdizen, “Mutual exclusivity loss for semi-supervised deep learning,” in (ICIP) (Accepted), IEEE International Conference on Image Processing
2016
Closest in time.