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We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN).
C. Farabet, C. Couprie, L. Najman, Y. LeCun, Learning hierarchical features for scene labeling, Pattern Analysis and Machine Intelligence, IEEE Transactions on 35 (8) (2013) 1915–1929
1929
Earlier work this paper cites.
A. Torralba, R. Fergus, W. Freeman, 80 million tiny images: A large data set for nonparametric object and scene recognition, Pattern Analysis and Machine Intelligence, IEEE Transactions on 30 (11) (2008) 1958–1970
1970
Earlier work this paper cites.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, Backpropagation applied to handwritten zip code recognition, in: Neural Comput., Vol. 1, 1989, pp. 541–551
1989
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (11) (1998) 2278–2324
1998
Earlier work this paper cites.
S. Godbole, S. Sarawagi, S. Chakrabarti, Scaling multi-class support vector machines using inter-class confusion, in: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, pp. 513–518
2002
Earlier work this paper cites.
S. Godbole, S. Sarawagi, Discriminative methods for multi-labeled classification, in: Advances in Knowledge Discovery and Data Mining, 2004, pp. 22–30
2004
Earlier work this paper cites.
M. Delakis, C. Garcia, text detection with convolutional neural networks., in: VISAPP (2), 2008, pp. 290–294
2008
Earlier work this paper cites.
G. Griffin, P. Perona, Learning and using taxonomies for fast visual categorization, in: Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, 2008, pp. 1–8
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, Imagenet: A large-scale hierarchical image database, in: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, 2009, pp. 248–255
2009
Earlier work this paper cites.
J. Deng, A. Berg, K. Li, L. Fei-Fei, What does classifying more than 10,000 image categories tell us?, in: Computer Vision – ECCV 2010, Vol. 6315, 2010, pp. 71–84
2010
Earlier work this paper cites.
S. Bengio, J. Weston, D. Grangier, Label embedding trees for large multi-class tasks, in: Advances in Neural Information Processing Systems 23, 2010, pp. 163–171
2010
Earlier work this paper cites.
M. Sindelar, R. K. Sitaraman, P. Shenoy, Sharing-aware algorithms for virtual machine colocation, in: Proceedings of the 23th annual ACM symposium on Parallelism in algorithms and architectures, 2011, pp. 367–378
2011
Earlier work this paper cites.
J. Deng, S. Satheesh, A. C. Berg, F. Li, Fast and balanced: Efficient label tree learning for large scale object recognition, in: NIPS, 2011, pp. 567–575
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems 25, 2012, pp. 1097–1105
2012
Cited alongside, same era.
T. Wang, D. J. Wu, A. Coates, A. Y. Ng, End-to-end text recognition with convolutional neural networks, in: Pattern Recognition (ICPR), 2012 21st International Conference on, IEEE, 2012, pp. 3304–3308
2012
Cited alongside, same era.
http://image-net.org/challenges/LSVRC/2012/index
2012
Cited alongside, same era.
D. Ciresan, U. Meier, J. Schmidhuber, Multi-column deep neural networks for image classification, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012, pp. 3642–3649
2012
Cited alongside, same era.
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, R. Fergus, Regularization of neural networks using dropconnect, in: Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013, pp. 1058–1066
A. Toshev, C. Szegedy, Deeppose: Human pose estimation via deep neural networks, in: Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014, pp. 1653–1660
2014
Later among the works it cites.
J. J. Tompson, A. Jain, Y. LeCun, C. Bregler, Joint training of a convolutional network and a graphical model for human pose estimation, in: Advances in Neural Information Processing Systems, 2014, pp. 1799–1807
2014
Later among the works it cites.
Y. Gong, L. Wang, R. Guo, S. Lazebnik, Multi-scale orderless pooling of deep convolutional activation features, in: Computer Vision – ECCV 2014, 2014, pp. 392–407
2014
Later among the works it cites.
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, A. Oliva, Learning deep features for scene recognition using places database, in: Advances in Neural Information Processing Systems, 2014
2014
Later among the works it cites.
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2013
Cited alongside, same era.
I. Goodfellow, D. Warde-farley, M. Mirza, A. Courville, Y. Bengio, Maxout networks, in: Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013, pp. 1319–1327
2013
Cited alongside, same era.
M. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in: Computer Vision – ECCV 2014, 2014, pp. 818–833
2014
Cited alongside, same era.
R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in: Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014, pp. 580–587
2014
Cited alongside, same era.
D. Erhan, C. Szegedy, A. Toshev, D. Anguelov, Scalable object detection using deep neural networks, in: Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, IEEE, 2014, pp. 2155–2162
2014
Cited alongside, same era.
W. Huang, Y. Qiao, X. Tang, Robust scene text detection with convolution neural network induced mser trees, in: Computer Vision–ECCV 2014, Springer, 2014, pp. 497–511
2014
Cited alongside, same era.
K. Simonyan, A. Zisserman, Two-stream convolutional networks for action recognition in videos, in: Advances in Neural Information Processing Systems, 2014, pp. 568–576
2014
Cited alongside, same era.
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, L. Fei-Fei, Large-scale video classification with convolutional neural networks, in: Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014, pp. 1725–1732
2014
Cited alongside, same era.
M. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in: Computer Vision – ECCV 2014, Vol. 8689, 2014, pp. 818–833
2014
Later among the works it cites.
W. Ouyang, X. Wang, X. Zeng, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, C.-C. Loy, et al., Deepid-net: Deformable deep convolutional neural networks for object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2403–2412
2015
Closest in time.
H. Li, Y. Li, F. Porikli, Robust online visual tracking with a single convolutional neural network, in: Computer Vision–ACCV 2014, Springer, 2015, pp. 194–209
2015
Closest in time.
L. Wang, T. Liu, G. Wang, K. L. Chan, Q. Yang, Video tracking using learned hierarchical features, Image Processing, IEEE Transactions on 24 (4) (2015) 1424–1435
2015
Closest in time.
B. Shuai, G. Wang, Z. Zuo, B. Wang, L. Zhao, Integrating parametric and non-parametric models for scene labeling, Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on 72 (2015) 50–8
2015
Closest in time.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9
2015
Closest in time.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778
2016
Closest in time.
Y. Tian, B. Fan, F. Wu, L2-net: Deep learning of discriminative patch descriptor in euclidean space, in: Computer Vision and Pattern Recognition, 2009. CVPR 2017. IEEE Conference on, 2017
2017
Closest in time.