Fetching the paper…
Reading the bibliography…
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices.
Using relevance to reduce network size automatically
Mozer, M. C. and Smolensky, P · 1989
Earlier work this paper cites.
Second order derivatives for network pruning: Optimal brain surgeon
Hassibi, B. and Stork, D. G · 1993
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
LeCun, Y., Bengio, Y., et al · 1995
Earlier work this paper cites.
An iterative pruning algorithm for feedforward neural networks
Castellano, G., Fanelli, A. M., and Pelillo, M · 1997
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
Collobert, R. and Weston, J · 2008
Earlier work this paper cites.
The group-lasso for generalized linear models: uniqueness of solutions and efficient algorithms
Roth, V. and Fischer, B · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Speeding up convolutional neural networks with low rank expansions
Jaderberg, M., Vedaldi, A., and Zisserman, A · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Channel-level acceleration of deep face representations
Polyak, A. and Wolf, L · 2015
Cited alongside, same era.
Learning the number of neurons in deep networks
Alvarez, J. M. and Salzmann, M · 2016
Cited alongside, same era.
Dynamic network surgery for efficient dnns
Guo, Y., Yao, A., and Chen, Y · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hu, H., Peng, R., Tai, Y.-W., and Tang, C.-K · 2016
Learning efficient convolutional networks through network slimming
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., and Zhang, C · 2017
Later among the works it cites.
Thinet: A filter level pruning method for deep neural network compression
Luo, J.-H., Wu, J., and Lin, W · 2017
Later among the works it cites.
Variational dropout sparsifies deep neural networks
Molchanov, D., Ashukha, A., and Vetrov, D · 2017
Later among the works it cites.
Sharma, A., Wolfe, N., and Raj, B · 2017
Later among the works it cites.
Auto-balanced filter pruning for efficient convolutional neural networks
Ding, X., Ding, G., Han, J., and Tang, S · 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.
Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P · 2016
Cited alongside, same era.
Pruning convolutional neural networks for resource efficient inference
Molchanov, P., Tyree, S., Karras, T., Aila, T., and Kautz, J · 2016
Cited alongside, same era.
Cnnpack: Packing convolutional neural networks in the frequency domain
Wang, Y., Xu, C., You, S., Tao, D., and Xu, C · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
Wen, W., Wu, C., Wang, Y., Chen, Y., and Li, H · 2016
Cited alongside, same era.
Structural compression of convolutional neural networks based on greedy filter pruning
Abbasi-Asl, R. and Yu, B · 2017
Cited alongside, same era.
Structured pruning of deep convolutional neural networks
Anwar, S., Hwang, K., and Sung, W · 2017
Cited alongside, same era.
Huang, Q., Zhou, K., You, S., and Neumann, U · 2018
Later among the works it cites.
Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference
Luo, J.-H. and Wu, J · 2018
Later among the works it cites.
Thinet: pruning cnn filters for a thinner net
Luo, J.-H., Zhang, H., Zhou, H.-Y., Xie, C.-W., Wu, J., and Lin, W · 2018
Later among the works it cites.
Globally soft filter pruning for efficient convolutional neural networks
Xu, K., Wang, X., Jia, Q., An, J., and Wang, D · 2018
Later among the works it cites.
Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
Ye, J., Lu, X., Lin, Z., and Wang, J. Z · 2018
Later among the works it cites.
Nisp: Pruning networks using neuron importance score propagation
Yu, R., Li, A., Chen, C.-F., Lai, J.-H., Morariu, V. I., Han, X., Gao, M., Lin, C.-Y., and Davis, L. S · 2018
Later among the works it cites.
Online filter weakening and pruning for efficient convnets
Zhou, Z., Zhou, W., Hong, R., and Li, H · 2018
Later among the works it cites.
Centripetal sgd for pruning very deep convolutional networks with complicated structure
Ding, X., Ding, G., Guo, Y., and Han, J · 2019
Closest in time.