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The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data.
1902
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, Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
A. Krizhevsky, G. Hinton, Learning multiple layers of features from tiny images, Tech. rep., Citeseer (2009)
2009
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, 2012, pp. 1097–1105
2012
Earlier work this paper cites.
M. D. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in: European conference on computer vision, Springer, 2014, pp. 818–833
2014
Earlier work this paper cites.
G. F. Montufar, R. Pascanu, K. Cho, Y. Bengio, On the number of linear regions of deep neural networks, in: Advances in neural information processing systems, 2014, pp. 2924–2932
2014
Earlier work this paper cites.
J. Ba, R. Caruana, Do deep nets really need to be deep?, in: Advances in neural information processing systems, 2014, pp. 2654–2662
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, G. Hinton, Deep learning, nature 521 (7553) (2015) 436
2015
Cited alongside, same era.
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
Cited alongside, same era.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al., Imagenet large scale visual recognition challenge, International Journal of Computer Vision 115 (3) (2015) 211–252
2015
Cited alongside, same era.
S. Liu, W. Deng, Very deep convolutional neural network based image classification using small training sample size, in: Pattern Recognition (ACPR), 2015 3rd IAPR Asian Conference on, IEEE, 2015, pp. 730–734
2015
Cited alongside, same era.
K. Sirinukunwattana, S. E. A. Raza, Y.-W. Tsang, D. R. Snead, I. A. Cree, N. M. Rajpoot, Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images, IEEE transactions on medical imaging 35 (5) (2016) 1196–1206
2016
Later among the works it cites.
K. He, G. Gkioxari, P. Dollár, R. Girshick, Mask r-cnn, in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961–2969
2017
Later among the works it cites.
B. Hariharan, P. Arbelaez, R. Girshick, J. Malik, Object instance segmentation and fine-grained localization using hypercolumns, IEEE transactions on pattern analysis and machine intelligence 39 (4) (2017) 627–639
2017
Later among the works it cites.
A. Bansal, C. Castillo, R. Ranjan, R. Chellappa, The do’s and don’ts for cnn-based face verification, in: 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), IEEE, 2017, pp. 2545–2554
2017
Later among the works it cites.
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S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning, 2015, pp. 448–456
2015
Cited alongside, same era.
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
Cited alongside, same era.
H. N. Mhaskar, T. Poggio, Deep vs. shallow networks: An approximation theory perspective, Analysis and Applications 14 (06) (2016) 829–848
2016
Cited alongside, same era.
Cited in the paper.
Cited in the paper.
J. Wu, Q. Zhang, G. Xu, Tiny imagenet challenge, cs231n, Stanford University
Cited in the paper.
S. S. Basha, S. Ghosh, K. K. Babu, S. R. Dubey, V. Pulabaigari, S. Mukherjee, Rccnet: An efficient convolutional neural network for histological routine colon cancer nuclei classification, in: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, 2018, pp. 1222–1227
2018
Later among the works it cites.
Q. Xu, M. Zhang, Z. Gu, G. Pan, Overfitting remedy by sparsifying regularization on fully-connected layers of cnns, Neurocomputing 328 (2019) 69–74
2019
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