Fetching the paper…
Reading the bibliography…
Normalization methods are essential components in convolutional neural networks (CNNs).
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Natural neural networks
G. Desjardins, K. Simonyan, R. Pascanu, et al · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Training deep networks with structured layers by matrix backpropagation
C. Ionescu, O. Vantzos, and C. Sminchisescu · 2015
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
Earlier work this paper cites.
Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
Earlier work this paper cites.
Semantic understanding of scenes through the ade20k dataset
B. Zhou, H. Zhao, X. Puig, T. Xiao, S. Fidler, A. Barriuso, and A. Torralba · 2016
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
Arbitrary style transfer in real-time with adaptive instance normalization
X. Huang and S. Belongie · 2017
Cited alongside, same era.
Batch renormalization: Towards reducing minibatch dependence in batch-normalized models
S. Ioffe · 2017
Cited alongside, same era.
Mmd gan: Towards deeper understanding of moment matching network
C.-L. Li, W.-C. Chang, Y. Cheng, Y. Yang, and B. Póczos · 2017
Cited alongside, same era.
Universal style transfer via feature transforms
Y. Li, C. Fang, J. Yang, Z. Wang, X. Lu, and M.-H. Yang · 2017
Do normalization layers in a deep convnet really need to be distinct?
P. Luo, Z. Peng, J. Ren, and R. Zhang · 2018
Later among the works it cites.
Batch-instance normalization for adaptively style-invariant neural networks
H. Nam and H.-E. Kim · 2018
Later among the works it cites.
Two at once: Enhancing learning and generalization capacities via ibn-net
X. Pan, P. Luo, J. Shi, and X. Tang · 2018
Later among the works it cites.
Whitening and coloring transform for gans
A. Siarohin, E. Sangineto, and N. Sebe · 2018
Later among the works it cites.
Learning to adapt structured output space for semantic segmentation
Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
G. Lin, A. Milan, C. Shen, and I. Reid · 2017
Cited alongside, same era.
Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2017
Cited alongside, same era.
Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
Cited alongside, same era.
Scene parsing through ade20k dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
Cited alongside, same era.
Decorrelated batch normalization
H. Lei, Y. Dawei, L. Bo, and D. Jia · 2018
Cited alongside, same era.
Batch kalman normalization: Towards training deep neural networks with micro-batches
G. Wang, J. Peng, P. Luo, X. Wang, and L. Lin · 2018
Later among the works it cites.
Group normalization
Y. Wu and K. He · 2018
Later among the works it cites.
Context encoding for semantic segmentation
H. Zhang, K. Dana, J. Shi, Z. Zhang, X. Wang, A. Tyagi, and A. Agrawal · 2018
Later among the works it cites.
Psanet: Point-wise spatial attention network for scene parsing
H. Zhao, Y. Zhang, S. Liu, J. Shi, C. Change Loy, D. Lin, and J. Jia · 2018
Later among the works it cites.
Mmdetection: Open mmlab detection toolbox and benchmark
K. Chen, J. Wang, J. Pang, Y. Cao, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Xu, et al · 2019
Closest in time.
Iterative normalization: Beyond standardization towards efficient whitening
L. Huang, Y. Zhou, F. Zhu, L. Liu, and L. Shao · 2019
Closest in time.
Differentiable learning-to-normalize via switchable normalization
P. Luo, J. Ren, and Z. Peng · 2019
Closest in time.
Wider or deeper: Revisiting the resnet model for visual recognition
Z. Wu, C. Shen, and A. Van Den Hengel · 2019
Closest in time.