Pruning from Scratch

Discrimination-aware channel pruning for deep neural networks

Z. Zhuang, M. Tan, B. Zhuang, J. Liu, Y. Guo, Q. Wu, J. Huang, and J. Zhu · 1909

Earlier work this paper cites.

Optimal brain damage

Y. LeCun, J. S. Denker, and S. A. Solla · 1990

Earlier work this paper cites.

Learning multiple layers of features from tiny images

A. Krizhevsky et al · 2009

Earlier work this paper cites.

Very deep convolutional networks for large-scale image recognition

Original

K. Simonyan and A. Zisserman · 2014

Earlier work this paper cites.

Learning both weights and connections for efficient neural network

S. Han, J. Pool, J. Tran, and W. Dally · 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.

Imagenet large scale visual recognition challenge

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015

Earlier work this paper cites.

Eie: efficient inference engine on compressed deep neural network

S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally · 2016

Earlier work this paper cites.

Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding

S. Han, H. Mao, and W. J. Dally · 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.

Pruning filters for efficient convnets

H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2016

Cited alongside, same era.

Sgdr: Stochastic gradient descent with warm restarts

I. Loshchilov and F. Hutter · 2016

Cited alongside, same era.

Rethinking the inception architecture for computer vision

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016

Cited alongside, same era.

Channel pruning for accelerating very deep neural networks

Y. He, X. Zhang, and J. Sun · 2017

Cited alongside, same era.

Mobilenets: Efficient convolutional neural networks for mobile vision applications

Original

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017

Cited alongside, same era.

Learning efficient convolutional networks through network slimming

Soft filter pruning for accelerating deep convolutional neural networks

Y. He, G. Kang, X. Dong, Y. Fu, and Y. Yang · 2018

Later among the works it cites.

Amc: Automl for model compression and acceleration on mobile devices

Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han · 2018

Later among the works it cites.

Darts: Differentiable architecture search

H. Liu, K. Simonyan, and Y. Yang · 2018

Later among the works it cites.

Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference

Original

J.-H. Luo and J. Wu · 2018

Later among the works it cites.

Mobilenetv2: Inverted residuals and linear bottlenecks

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018

Later among the works it cites.

Netadapt: Platform-aware neural network adaptation for mobile applications