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Network compression has been widely studied since it is able to reduce the memory and computation cost during inference.
Optimal brain damage
LeCun, Y., Denker, J. S., and Solla, S. A · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
Hassibi, B. and Stork, D. G · 1993
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Kai Li, and Li Fei-Fei · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Dynamic network surgery for efficient dnns
Guo, Y., Yao, A., and Chen, Y · 2016
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Han, S., Mao, H., and Dally, W. J · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Fast convnets using group-wise brain damage
Lebedev, V. and Lempitsky, V · 2016
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Learning structured sparsity in deep neural networks
Wen, W., Wu, C., Wang, Y., Chen, Y., and Li, H · 2016
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Dong, X., Chen, S., and Pan, S · 2017
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Channel pruning for accelerating very deep neural networks
He, Y., Zhang, X., and Sun, J · 2017
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Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P · 2017
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Learning efficient convolutional networks through network slimming
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., and Zhang, C · 2017
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Thinet: A filter level pruning method for deep neural network compression
Luo, J.-H., Wu, J., and Lin, W · 2017
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Pruning convolutional neural networks for resource efficient inference
Molchanov, P., Tyree, S., Karras, T., Aila, T., and Kautz, J · 2017
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K · 2017
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Data-driven sparse structure selection for deep neural networks
Huang, Z. and Wang, N · 2018
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Accelerating convolutional networks via global & dynamic filter pruning
Lin, S., Ji, R., Li, Y., Wu, Y., Huang, F., and Zhang, B · 2018
Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks
You, Z., Yan, K., Ye, J., Ma, M., and Wang, P · 2019
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Slimmable neural networks
Yu, J., Yang, L., Xu, N., Yang, J., and Huang, T · 2019
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Feature selective anchor-free module for single-shot object detection
Zhu, C., He, Y., and Savvides, M · 2019
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Learning filter pruning criteria for deep convolutional neural networks acceleration
He, Y., Ding, Y., Liu, P., Zhu, L., Zhang, H., and Yang, Y · 2020
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Probabilistic anchor assignment with iou prediction for object detection
Kim, K. and Lee, H. S · 2020
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Benanza: Automatic ubenchmark generation to compute ”lower-bound” latency and inform optimizations of deep learning models on gpus
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
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Faster gaze prediction with dense networks and fisher pruning
Theis, L., Korshunova, I., Tejani, A., and Huszár, F · 2018
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Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
Ye, J., Lu, X., Lin, Z., and Wang, J. Z · 2018
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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
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Discrimination-aware channel pruning for deep neural networks
Zhuang, Z., Tan, M., Zhuang, B., Liu, J., Guo, Y., Wu, Q., Huang, J., and Zhu, J · 2018
Cited alongside, same era.
Filter pruning via geometric median for deep convolutional neural networks acceleration
He, Y., Liu, P., Wang, Z., Hu, Z., and Yang, Y · 2019
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Li, C., Dakkak, A., Xiong, J., and Hwu, W · 2020
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Hrank: Filter pruning using high-rank feature map
Lin, M., Ji, R., Wang, Y., Zhang, Y., Zhang, B., Tian, Y., and Shao, L · 2020
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Neural network pruning with residual-connections and limited-data
Luo, J.-H. and Wu, J · 2020
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Designing network design spaces
Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., and Dollár, P · 2020
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Hetconv: Beyond homogeneous convolution kernels for deep cnns
Singh, P., Verma, V. K., Rai, P., and Namboodiri, V. P · 2020
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Woodfisher: Efficient second-order approximation for neural network compression
Singh, S. P. and Alistarh, D · 2020
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HAT: Hardware-aware transformers for efficient natural language processing
Wang, H., Wu, Z., Liu, Z., Cai, H., Zhu, L., Gan, C., and Han, S · 2020
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Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection
Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S. Z · 2020
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Accelerating sparse deep neural networks
Mishra, A. K., Latorre, J. A., Pool, J., Stosic, D., Stosic, D., Venkatesh, G., Yu, C., and Micikevicius, P · 2021
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Learning n:m fine-grained structured sparse neural networks from scratch
Zhou, A., Ma, Y., Zhu, J., Liu, J., Zhang, Z., Yuan, K., Sun, W., and Li, H · 2021
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