Fetching the paper…
Reading the bibliography…
Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks.
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting., Journal of machine learning research 15 (1) (2014) 1929–1958
1958
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.
L. Fei-Fei, R. Fergus, P. Perona, One-shot learning of object categories, IEEE transactions on pattern analysis and machine intelligence 28 (4) (2006) 594–611
2006
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, 1097–1105, 2012
2012
Earlier work this paper cites.
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), 1058–1066, 2013
2013
Earlier work this paper cites.
N. Srivastava, R. R. Salakhutdinov, Discriminative transfer learning with tree-based priors, in: Advances in Neural Information Processing Systems, 2094–2102, 2013
2013
Earlier work this paper cites.
T. Xiao, J. Zhang, K. Yang, Y. Peng, Z. Zhang, Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification, MM ’14 Proceedings of the ACM International Conference on Multime d (2014) 177–186
2014
Earlier work this paper cites.
S. John Walker, Big data: A revolution that will transform how we live, work, and think, 2014
2014
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks?, in: Advances in neural information processing systems, 3320–3328, 2014
2014
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.
R. Girshick, Fast R-CNN, in: 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, 2015
2015
Cited alongside, same era.
Z. Yan, H. Zhang, R. Piramuthu, V. Jagadeesh, D. DeCoste, W. Di, Y. Yu, HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition, in: 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, 2015
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, 770–778, 2016
2016
Later among the works it cites.
W. Rawat, Z. Wang, Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review, Neural Computation 29 (9) (2017) 2352–2449
2017
Later among the works it cites.
K. Shmelkov, C. Schmid, K. Alahari, Incremental Learning of Object Detectors without Catastrophic Forgetting, in: 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017
2017
Later among the works it cites.
S.-A. Rebuffi, A. Kolesnikov, C. H. Lampert, iCaRL: Incremental classifier and representation learning, in: Proc. CVPR, 2017
2017
Later among the works it cites.
S. S. Sarwar, P. Panda, K. Roy, Gabor filter assisted energy efficient fast learning Convolutional Neural Networks, in: 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), IEEE, 1–6, 2017a
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
P. Kontschieder, M. Fiterau, A. Criminisi, S. R. Bulo, Deep neural decision forests, in: Computer Vision (ICCV), 2015 IEEE International Conference on, IEEE, 1467–1475, 2015
2015
Cited alongside, same era.
A. Vedaldi, K. Lenc, Matconvnet: Convolutional neural networks for matlab, in: Proceedings of the 23rd ACM international conference on Multimedia, ACM, 689–692, 2015
2015
Cited alongside, same era.
S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning, 448–456, 2015
2015
Cited alongside, same era.
L. Hertel, E. Barth, T. Kaster, T. Martinetz, Deep convolutional neural networks as generic feature extractors, in: 2015 International Joint Conference on Neural Networks (IJCNN), IEEE, 2015
2015
Cited alongside, same era.
A. Krizhevsky, G. Hinton, Learning multiple layers of features from tiny images
Cited in the paper.
Cited in the paper.
Z. Li, D. Hoiem, Learning without forgetting, IEEE Transactions on Pattern Analysis and Machine Intelligence
Cited in the paper.
Cited in the paper.
2017
Later among the works it cites.
P. Panda, K. Roy, Semantic driven hierarchical learning for energy-efficient image classification, in: 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE), IEEE, 1582–1587, 2017
2017
Later among the works it cites.
MATLAB, version 9.2.0 (R2017a), The MathWorks Inc., Natick, Massachusetts, 2017
2017
Later among the works it cites.