The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Open accessAbstract
We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter level pruning, i.e., the whole filter would be discarded if it is less important. Our method does not change the original network structure, thus it can be perfectly supported by any off-the-shelf deep learning libraries. We formally establish filter pruning as an optimization problem, and reveal that we need to prune filters based on statistics information computed from its next layer, not the current layer, which differentiates ThiNet from existing methods. Experimental results demonstrate the effectiveness of this strategy, which has advanced the state-of-the-art. We also show the performance of ThiNet on ILSVRC-12 benchmark. ThiNet achieves 3.31 x FLOPs reduction and 16.63× compression on VGG-16, with only 0.52% top-5 accuracy drop. Similar experiments with ResNet-50 reveal that even for a compact network, ThiNet can also reduce more than half of the parameters and FLOPs, at the cost of roughly 1% top-5 accuracy drop. Moreover, the original VGG-16 model can be further pruned into a very small model with only 5.05MB model size, preserving AlexNet level accuracy but showing much stronger generalization ability.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461
Compressing Neural Networks with the Hashing Trick
Chen, W., Wilson, J. T., Tyree, S., Weinberger, K. Q., & Chen, Y. (2015) · arXiv · doi:10.48550/arxiv.1504.04788 · Cited by 974
Research works printed but not linked: 28
- 10.1109/CVPR.2016.521 · DOI · Paper page
- wen, 2016, Learning structured sparsity in deep neural networks, NIPS, 2074
- wah, 2011, The Caltech-UCSD birds-200–2011 dataset, Technical Report CNS-TR-2011-001
- 10.1109/CVPR.2015.7298594 · DOI · Paper page
- 10.1109/CVPR.2016.319 · DOI · Paper page
- han, 2015, Learning both weights and connections for efficient neural network, NIPS, 1135
- hinton, 1986, Learning distributed representations of concepts, CogSci, 1
- hu, 2016, Network trimming A data-driven neuron pruning approach towards efficient deep architectures, 1
- iandola, 2016, SqueezeNet AlexNet-level accuracy with 50x fewer parameters and, 1
- 10.1109/ICCV.2015.277 · DOI · Paper page
- jia, 2014, Learning distributed representations of concepts, ACM MM, 675
- 10.1109/CVPR.2016.280 · DOI · Paper page
- simonyan, 2015, Very deep convolutional networks for large-scale image recognition, ICLRE, 1
- denil, 2013, Predicting parameters in deep learning, NIPS, 2148
- 10.1109/TIT.2008.929958 · DOI · Paper page
- sindhwani, 2015, Structured transforms for small-footprint deep learning, NIPS, 3088
- denton, 2014, Exploiting linear structure within convolutional networks for efficient evaluation, NIPS, 1269
- gong, 2014, Compressing deep convolutional networks using vector quantization, 1
- girshick fast, 2015, ICCV, 1440
- 10.1162/089976698300017124 · DOI
- han, 2016, Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, ICLRE, 1
- 10.1109/TPAMI.2013.50 · DOI · Paper page
- lecun, 1990, Optimal brain damage, NIPS, 598
- lin, 2013, Network in Network, 1
- li, 2017, Pruning filters for efficient ConvNets, ICLRE, 1
- 10.1109/ICCV.2015.178 · DOI · Paper page
- molchanov, 2017, Pruning convolutional neural networks for resource efficient transfer learning, ICLRE, 1
- 10.1109/CVPR.2009.5206537 · DOI · Paper page
Cited by
GhostNet: More Features From Cheap Operations
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00165 · Cited by 5,222
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J., & Carbin, M. (2018) · arXiv · doi:10.48550/arxiv.1803.03635 · Cited by 1,529
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
He, Y., Lin, J., Liu, Z., Wang, H., Li, L.-J., & Han, S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_48 · Cited by 1,400
A Survey of Quantization Methods for Efficient Neural Network Inference
Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., & Keutzer, K. (2022) · Low-Power Computer Vision · doi:10.1201/9781003162810-13 · Cited by 1,219
“Pruning methods can be broadly categorized into unstructured pruning [49, 84, 135, 139, 184, 244], and structured pruning [89, 104, 151, 160, 261, 262, 267].”
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
He, Y., Kang, G., Dong, X., Fu, Y., & Yang, Y. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/309 · Cited by 1,020
Metadata
- Authors
- Luo, J.-H., Wu, J., & Lin, W.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5068-5076
- OpenAlex
- W2964233199
- Cited by
- 1,832
- References
- 8
- FWCI
- 51.69
- Type
- conference paper
- Language
- en
- Open access
- green
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- Luo, J.-H., Wu, J., & Lin, W. (2017). ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.541
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