The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning
Abstract
We introduce a novel technique for knowledge transfer, where knowledge from a pretrained deep neural network (DNN) is distilled and transferred to another DNN. As the DNN performs a mapping from the input space to the output space through many layers sequentially, we define the distilled knowledge to be transferred in terms of flow between layers, which is calculated by computing the inner product between features from two layers. When we compare the student DNN and the original network with the same size as the student DNN but trained without a teacher network, the proposed method of transferring the distilled knowledge as the flow between two layers exhibits three important phenomena: (1) the student DNN that learns the distilled knowledge is optimized much faster than the original model, (2) the student DNN outperforms the original DNN, and (3) the student DNN can learn the distilled knowledge from a teacher DNN that is trained at a different task, and the student DNN outperforms the original DNN that is trained from scratch.
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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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
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
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv · doi:10.48550/arxiv.1603.05027 · Cited by 10,665
Research works printed but not linked: 16
- krähenbühl, 2015, Data-dependent initializations of convolutional neural networks
- krizhevsky, 2009, Learning multiple layers of features from tiny images
- mishkin, 2015, All you need is a good init
- noh, 2015, Image question answering using convolutional neural network with dynamic parameter prediction
- zhou, 2015, Simple baseline for visual question answering
- duchi, 2011, Adaptive subgradient methods for online learning and stochastic optimization, Journal of Machine Learning Research, 12, 2121
- chen, 2015, Net2net Accelerating learning via knowledge transfer
- gatys, 2015, A neural algorithm of artistic style
- erhan, 2010, Why does unsupervised pre-training help deep learning?, Journal of Machine Learning Research, 11, 625
- glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, AISTATS, 9, 249
- branson, 2014, Bird species categorization using pose normalized deep convolutional nets
- romero, 2015, Fitnets: Hints for thin deep nets, Proceedings of ICL
- saxe, 2013, Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
- veit, 2016, Residual networks are exponential ensembles of relatively shallow networks
- 10.1109/CVPR.2015.7298594 · DOI · Paper page
- wah, 2011, The Caltech-UCSD Birds-200-2011 Dataset, Technical Report
Cited by
Knowledge Distillation: A Survey
Gou, J., Yu, B., Maybank, S. J., & Tao, D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01453-z · Cited by 3,931
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
“[94, 148, 187] use logits (the soft probabilities) as the source of knowledge, while [3, 200, 261] try to leverage the knowledge from intermediate layers.”
Improved Knowledge Distillation via Teacher Assistant
Mirzadeh, S. I., Farajtabar, M., Li, A., Levine, N., Matsukawa, A., & Ghasemzadeh, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5963 · Cited by 101
“In the ’FSP’ method, a channel-wise correlation matrix is used as the medium of knowledge transfer (Yim et al. 2017).”
BackgroundMethodDecoupled Knowledge Distillation
Zhao, B., Cui, Q., Song, R., Qiu, Y., & Liang, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01165 · Cited by 965
Rethinking Few-Shot Image Classification: A Good Embedding is All You Need?
Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B., & Isola, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58568-6_16 · Cited by 952
Metadata
- Authors
- Yim, J., Joo, D., Bae, J., & Kim, J.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 7130-7138
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2739879705
- Cited by
- 1,570
- References
- 13
- FWCI
- 45.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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