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Recently many plug-and-play self-attention modules (SAMs) are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs).
S. You, T. Huang, M. Yang, F. Wang, C. Qian, and C. Zhang, “Greedynas: Towards fast one-shot nas with greedy supernet,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 1999–2008
2008
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International Journal of Computer Vision , vol. 88, no. 2, pp. 303–338, Jun. 2010
2010
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV) , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International journal of computer vision , vol. 111, no. 1, pp. 98–136, 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and 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
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 589–597
2016
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 618–626
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Earlier work this paper cites.
S. Woo, J. Park, J.-Y. Lee, and I. So Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 3–19
2018
Earlier work this paper cites.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7794–7803
2018
Earlier work this paper cites.
S. Bianco, R. Cadene, L. Celona, and P. Napoletano, “Benchmark analysis of representative deep neural network architectures,” IEEE Access , vol. 6, pp. 64 270–64 277, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean, “Efficient neural architecture search via parameters sharing,” in International Conference on Machine Learning , 2018, pp. 4095–4104
2018
Cited alongside, same era.
L. et al., “Darts: Differentiable architecture search,” 2018
2018
Cited alongside, same era.
X. Cao, Z. Wang, Y. Zhao, and F. Su, “Scale aggregation network for accurate and efficient crowd counting,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 734–750
2018
Cited alongside, same era.
Y. Li, X. Zhang, and D. Chen, “Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1091–1100
2018
Cited alongside, same era.
W. He, M. Wu, M. Liang, and S.-K. Lam, “Cap: Context-aware pruning for semantic segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2020, pp. 960–969
2020
Later among the works it cites.
P. Vidnerová and R. Neruda, Multi-objective Evolution for Deep Neural Network Architecture Search , 2020
2020
Later among the works it cites.
Z. Guo, X. Zhang, H. Mu, W. Heng, Z. Liu, Y. Wei, and J. Sun, “Single path one-shot neural architecture search with uniform sampling,” in European Conference on Computer Vision . Springer, 2020, pp. 544–560
2020
Later among the works it cites.
2020
Later among the works it cites.
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2018
Cited alongside, same era.
H. Lin and S. Jegelka, “Resnet with one-neuron hidden layers is a universal approximator,” Advances in Neural Information Processing Systems , vol. 31, pp. 6169–6178, 2018
2018
Cited alongside, same era.
G. Bender, P.-J. Kindermans, B. Zoph, V. Vasudevan, and Q. Le, “Understanding and simplifying one-shot architecture search,” in International Conference on Machine Learning , 2018, pp. 550–559
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Y. Cao, J. Xu, S. Lin, F. Wei, and H. Hu, “Gcnet: Non-local networks meet squeeze-excitation networks and beyond,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
Cited alongside, same era.
H. Lee, H.-E. Kim, and H. Nam, “Srm: A style-based recalibration module for convolutional neural networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1854–1862
2019
Cited alongside, same era.
Y. He, P. Liu, Z. Wang, Z. Hu, and Y. Yang, “Filter pruning via geometric median for deep convolutional neural networks acceleration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 4340–4349
2019
Cited alongside, same era.
2019
Cited alongside, same era.
B. Ma, Y. Zhao, Y. Yang, X. Zhang, X. Dong, D. Zeng, S. Ma, and S. Li, “Mri image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation,” Computerized Medical Imaging and Graphics , vol. 86, p. 101800, 2020
2020
Later among the works it cites.
L. Liu, J. Chen, H. Wu, T. Chen, G. Li, and L. Lin, “Efficient crowd counting via structured knowledge transfer,” in ACM International Conference on Multimedia , 2020
2020
Later among the works it cites.
V. Ramanujan, M. Wortsman, A. Kembhavi, A. Farhadi, and M. Rastegari, “What’s hidden in a randomly weighted neural network?” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
Later among the works it cites.
T. Chen, J. Frankle, S. Chang, S. Liu, Y. Zhang, Z. Wang, and M. Carbin, “The lottery ticket hypothesis for pre-trained bert networks,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 15 834–15 846. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/b6af2c9703f203a2794be03d443af2e3-Paper.pdf
2020
Later among the works it cites.
E. Malach, G. Yehudai, S. Shalev-Schwartz, and O. Shamir, “Proving the lottery ticket hypothesis: Pruning is all you need,” in Proceedings of the 37th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 13–18 Jul 2020, pp. 6682–6691. [Online]. Available: https://proceedings.mlr.press/v119/malach20a.html
2020
Later among the works it cites.
L. Orseau, M. Hutter, and O. Rivasplata, “Logarithmic pruning is all you need,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 2925–2934. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/1e9491470749d5b0e361ce4f0b24d037-Paper.pdf
2020
Later among the works it cites.
A. Pensia, S. Rajput, A. Nagle, H. Vishwakarma, and D. Papailiopoulos, “Optimal lottery tickets via subsetsum: Logarithmic over-parameterization is sufficient,” Advances in neural information processing systems , 2020
2020
Later among the works it cites.
Z. Huang, W. Shao, X. Wang, L. Lin, and P. Luo, “Rethinking the pruning criteria for convolutional neural network,” in Advances in Neural Information Processing Systems , A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., 2021. [Online]. Available: https://openreview.net/forum?id=HL_4vjPTdtp
2021
Later among the works it cites.
D. Wang, C. Gong, M. Li, Q. Liu, and V. Chandra, “Alphanet: Improved training of supernets with alpha-divergence,” in Proceedings of the 38th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, M. Meila and T. Zhang, Eds., vol. 139. PMLR, 18–24 Jul 2021, pp. 10 760–10 771. [Online]. Available: https://proceedings.mlr.press/v139/wang21i.html
2021
Later among the works it cites.
S. Liang, Y. Khoo, and H. Yang, “Drop-activation: Implicit parameter reduction and harmonious regularization,” Communications on Applied Mathematics and Computation , vol. 3, no. 2, pp. 293–311, 2021
2021
Later among the works it cites.
X. Chen, Y. Cheng, S. Wang, Z. Gan, Z. Wang, and J. Liu, “Early{bert}: Efficient {bert} training via early-bird lottery tickets,” 2021. [Online]. Available: https://openreview.net/forum?id=I-VfjSBzi36
2021
Later among the works it cites.