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The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs.
Optimal brain damage,
Y. LeCun, J. S. Denker, S. A. Solla, · 1989
Earlier work this paper cites.
Second order derivatives for network pruning: Optimal brain surgeon,
B. Hassibi, D. G. Stork, · 1992
Earlier work this paper cites.
Network information criterion-determining the number of hidden units for an artificial neural network model,
N. Murata, S. Yoshizawa, S. Amari, · 1994
Earlier work this paper cites.
Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,
S. Lazebnik, C. Schmid, J. Ponce, · 2006
Earlier work this paper cites.
What, where and who? Classifying events by scene and object recognition,
L. Li, F. Li, · 2007
Earlier work this paper cites.
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization,
J. Elson, J. R. Douceur, J. Howell, J. Saul, · 2007
Earlier work this paper cites.
Automated flower classification over a large number of classes,
M. Nilsback, A. Zisserman, · 2008
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, S. Belongie, The Caltech-UCSD Birds-200-2011 Dataset, Technical Report CNS-TR-2011-001, California Institute of Technology, 2011
2011
Earlier work this paper cites.
Predicting parameters in deep learning,
M. Denil, B. Shakibi, L. Dinh, M. Ranzato, N. de Freitas, · 2013
Earlier work this paper cites.
S. Maji, E. Rahtu, J. Kannala, M. B. Blaschko, A. Vedaldi, Fine-Grained Visual Classification of Aircraft, Technical Report, 2013
2013
Earlier work this paper cites.
3d object representations for fine-grained categorization,
J. Krause, M. Stark, J. Deng, L. Fei-Fei, · 2013
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, W. Samek, · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network,
S. Han, J. Pool, J. Tran, W. J. Dally, · 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. S. Bernstein, A. C. Berg, F. Li, · 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, W. J. Dally, · 2016
Earlier work this paper cites.
Deep residual learning for image recognition,
K. He, X. Zhang, S. Ren, J. Sun, · 2016
Earlier work this paper cites.
Efficient processing of deep neural networks: A tutorial and survey,
V. Sze, Y. Chen, T. Yang, J. S. Emer, · 2017
Earlier work this paper cites.
Explaining nonlinear classification decisions with deep taylor decomposition,
G. Montavon, S. Lapuschkin, A. Binder, W. Samek, K.-R. Müller, · 2017
Cited alongside, same era.
Pruning convolutional neural networks for resource efficient transfer learning,
P. Molchanov, S. Tyree, T. Karras, T. Aila, J. Kautz, · 2017
Cited alongside, same era.
meprop: Sparsified back propagation for accelerated deep learning with reduced overfitting,
X. Sun, X. Ren, S. Ma, H. Wang, · 2017
Cited alongside, same era.
Pruning filters for efficient convnets,
H. Li, A. Kadav, I. Durdanovic, H. Samet, H. P. Graf, · 2017
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned,
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, K.-R. Müller, · 2017
Cited alongside, same era.
Sparse deep transfer learning for convolutional neural network,
Compressing by learning in a low-rank and sparse decomposition form,
K. Guo, X. Xie, X. Xu, X. Xing, · 2019
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LightweightNet: Toward fast and lightweight convolutional neural networks via architecture distillation,
T. Xu, P. Yang, X. Zhang, C. Liu, · 2019
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Importance estimation for neural network pruning,
P. Molchanov, A. Mallya, S. Tyree, I. Frosio, J. Kautz, · 2019
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Transfer channel pruning for compressing deep domain adaptation models,
C. Yu, J. Wang, Y. Chen, X. Qin, · 2019
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Channel pruning based on mean gradient for accelerating convolutional neural networks,
C. Liu, H. Wu, · 2019
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ThiNet: Pruning CNN filters for a thinner net,
J.-H. Luo, H. Zhang, H.-Y. Zhou, C.-W. Xie, J. Wu, W. Lin, · 2019
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J. Liu, Y. Wang, Y. Qiao, · 2017
Cited alongside, same era.
Recent advances in convolutional neural networks,
J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, T. Chen, · 2018
Cited alongside, same era.
Model compression and acceleration for deep neural networks: The principles, progress, and challenges,
Y. Cheng, D. Wang, P. Zhou, T. Zhang, · 2018
Cited alongside, same era.
Methods for interpreting and understanding deep neural networks,
G. Montavon, W. Samek, K.-R. Müller, · 2018
Cited alongside, same era.
Shufflenet: An extremely efficient convolutional neural network for mobile devices,
X. Zhang, X. Zhou, M. Lin, J. Sun, · 2018
Cited alongside, same era.
NISP: pruning networks using neuron importance score propagation,
R. Yu, A. Li, C. Chen, J. Lai, V. I. Morariu, X. Han, M. Gao, C. Lin, L. S. Davis, · 2018
Cited alongside, same era.
Structured probabilistic pruning for convolutional neural network acceleration,
H. Wang, Q. Zhang, Y. Wang, H. Hu, · 2018
Cited alongside, same era.
Nest: A neural network synthesis tool based on a grow-and-prune paradigm,
X. Dai, H. Yin, N. K. Jha, · 2019
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W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, K.-R. Müller (Eds.), Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, volume 11700 of Lecture Notes in Computer Science
2019
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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods,
M. Hägele, P. Seegerer, S. Lapuschkin, M. Bockmayr, W. Samek, F. Klauschen, K.-R. Müller, A. Binder, · 2020
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Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images,
P. Seegerer, A. Binder, R. Saitenmacher, M. Bockmayr, M. Alber, P. Jurmeister, F. Klauschen, K.-R. Müller, · 2020
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Toward interpretable machine learning: Transparent deep neural networks and beyond,
W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, K.-R. Müller, · 2020
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Compact and computationally efficient representation of deep neural networks,
S. Wiedemann, K.-R. Müller, W. Samek, · 2020
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Deep neural network compression by in-parallel pruning-quantization,
F. Tung, G. Mori, · 2020
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Compressing the CNN architecture for in-air handwritten Chinese character recognition,
J. Gan, W. Wang, K. Lu, · 2020
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M. Guillemot, C. Heusele, R. Korichi, S. Schnebert, L. Chen, · 2020
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Learning structured sparsity in deep neural networks,
W. Wen, C. Wu, Y. Wang, Y. Chen, H. Li, · 2082
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