Built on

Y. LeCun, Y. Bengio et al. , “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks , vol. 3361, no. 10, p. 1995, 1995
1995
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Master’s thesis, Department of Computer Science, University of Toronto , 2009
2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105
2012
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR , vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” CoRR , vol. abs/1409.4842, 2014
Original
2014
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” CoRR , vol. abs/1411.1792, 2014
Original
2014
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR , vol. abs/1512.03385, 2015
Original
2015
Earlier work this paper cites.
X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” CoRR , vol. abs/1506.04214, 2015
Original
2015
Earlier work this paper cites.
R. K. Srivastava, K. Greff, and J. Schmidhuber, “Highway networks,” CoRR , vol. abs/1505.00387, 2015
Original
2015
Earlier work this paper cites.
S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” CoRR , vol. abs/1506.01497, 2015
Original
2015
Earlier work this paper cites.
N. Ballas, L. Yao, C. Pal, and A. C. Courville, “Delving deeper into convolutional networks for learning video representations,” CoRR , vol. abs/1511.06432, 2015
Original
2015
Earlier work this paper cites.
G. Lebanon and S. V. N. Vishwanathan, Eds., Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2015, San Diego, California, USA, May 9-12, 2015 , ser. JMLR Workshop and Conference Proceedings, vol. 38. JMLR.org, 2015. [Online]. Available: http://jmlr.org/proceedings/papers/v38/
2015
Earlier work this paper cites.

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G. Huang, Z. Liu, and K. Q. Weinberger, “Densely connected convolutional networks,” CoRR , vol. abs/1608.06993, 2016
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Then

K. He, G. Gkioxari, P. Dollár, and R. B. Girshick, “Mask R-CNN,” CoRR , vol. abs/1703.06870, 2017
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Later among the works it cites.
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in CVPR, Workshops , July 2017
2017
Later among the works it cites.
X. Fu, J. Huang, D. Zeng, Y. Huang, X. Ding, and J. Paisley, “Removing rain from single images via a deep detail network,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 . IEEE Computer Society, 2017, pp. 1715–1723
2017
Later among the works it cites.
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Later among the works it cites.

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2021

RegNet: Self-Regulated Network for Image Classification

Xu, Jing, Pan, Yu, Pan, Xinglin et al.

Understand

The ResNet and its variants have achieved remarkable successes in various computer vision tasks.

Despite its success in making gradient flow through building blocks, the simple shortcut connection mechanism limits the ability of re-exploring new potentially complementary features due to the additive function.
To address this issue, in this paper, we propose to introduce a regulator module as a memory mechanism to extract complementary features, which are further fed to the ResNet.
In particular, the regulator module is composed of convolutional RNNs (e.g., Convolutional LSTMs or Convolutional GRUs), which are shown to be good at extracting Spatio-temporal information.

Built on

Y. LeCun, Y. Bengio et al. , “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks , vol. 3361, no. 10, p. 1995, 1995
1995
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Master’s thesis, Department of Computer Science, University of Toronto , 2009
2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105
2012
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR , vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” CoRR , vol. abs/1409.4842, 2014
Original
2014
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” CoRR , vol. abs/1411.1792, 2014
Original
2014
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR , vol. abs/1512.03385, 2015
Original
2015
Earlier work this paper cites.
X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” CoRR , vol. abs/1506.04214, 2015
Original
2015
Earlier work this paper cites.
R. K. Srivastava, K. Greff, and J. Schmidhuber, “Highway networks,” CoRR , vol. abs/1505.00387, 2015
Original
2015
Earlier work this paper cites.
S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” CoRR , vol. abs/1506.01497, 2015
Original
2015
Earlier work this paper cites.
N. Ballas, L. Yao, C. Pal, and A. C. Courville, “Delving deeper into convolutional networks for learning video representations,” CoRR , vol. abs/1511.06432, 2015
Original
2015
Earlier work this paper cites.
G. Lebanon and S. V. N. Vishwanathan, Eds., Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2015, San Diego, California, USA, May 9-12, 2015 , ser. JMLR Workshop and Conference Proceedings, vol. 38. JMLR.org, 2015. [Online]. Available: http://jmlr.org/proceedings/papers/v38/
2015
Earlier work this paper cites.

Similar

G. Huang, Z. Liu, and K. Q. Weinberger, “Densely connected convolutional networks,” CoRR , vol. abs/1608.06993, 2016
Original
2016
Cited alongside, same era.
S. Zagoruyko and N. Komodakis, “Wide residual networks,” CoRR , vol. abs/1605.07146, 2016
Original
2016
Cited alongside, same era.
C. Szegedy, S. Ioffe, and V. Vanhoucke, “Inception-v4, inception-resnet and the impact of residual connections on learning,” CoRR , vol. abs/1602.07261, 2016
Original
2016
Cited alongside, same era.
S. Xie, R. B. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” CoRR , vol. abs/1611.05431, 2016
Original
2016
Cited alongside, same era.
F. Shen, R. Gan, and G. Zeng, “Weighted residuals for very deep networks,” international conference on systems , pp. 936–941, 2016

Then

K. He, G. Gkioxari, P. Dollár, and R. B. Girshick, “Mask R-CNN,” CoRR , vol. abs/1703.06870, 2017
Original
2017
Later among the works it cites.
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in CVPR, Workshops , July 2017
2017
Later among the works it cites.
X. Fu, J. Huang, D. Zeng, Y. Huang, X. Ding, and J. Paisley, “Removing rain from single images via a deep detail network,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 . IEEE Computer Society, 2017, pp. 1715–1723
2017
Later among the works it cites.
T. Zhang, G. Qi, B. Xiao, and J. Wang, “Interleaved group convolutions for deep neural networks,” CoRR , vol. abs/1707.02725, 2017
Original
2017
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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2016

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C. Ledig, L. Theis, F. Huszar, J. Caballero, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” CoRR , vol. abs/1609.04802, 2016

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2016

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G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, “Deep networks with stochastic depth,” CoRR , vol. abs/1603.09382, 2016

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2016

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M. Siam, S. Valipour, M. Jägersand, and N. Ray, “Convolutional gated recurrent networks for video segmentation,” CoRR , vol. abs/1611.05435, 2016

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B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” CoRR , vol. abs/1707.07012, 2017

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Y. Chen, J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng, “Dual path networks,” CoRR , vol. abs/1707.01629, 2017. [Online]. Available: http://arxiv.org/abs/1707.01629

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T. Lin, P. Goyal, R. B. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” CoRR , vol. abs/1708.02002, 2017

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2018