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Benefiting from well-trained deep neural networks (DNNs), model compression have captured special attention for computing resource limited equipment, especially edge devices.
A comprehensive overhaul of feature distillation,
B. Heo, J. Kim, S. Yun, H. Park, N. Kwak, J. Y. Choi, · 1930
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Graph mining: Laws, generators, and algorithms,
D. Chakrabarti, C. Faloutsos, · 2006
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M.-E. Nilsback, A. Zisserman, · 2006
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Learning multiple layers of features from tiny images (2009) 1–60
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The german traffic sign recognition benchmark: a multi-class classification competition,
J. Stallkamp, M. Schlipsing, J. Salmen, C. Igel, · 2011
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Imagenet classification with deep convolutional neural networks,
A. Krizhevsky, I. Sutskever, G. E. Hinton, · 2012
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Linkbreaker: Breaking the backdoor-trigger link in dnns via neurons consistency check,
Z. Chen, S. Wang, A. Fu, Y. Gao, S. Yu, R. H. Deng, · 2014
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Caffe: Convolutional architecture for fast feature embedding,
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, · 2014
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Distilling the knowledge in a neural network,
G. Hinton, O. Vinyals, J. Dean, · 2015
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Fitnets: Hints for thin deep nets,
R. Adriana, B. Nicolas, K. S. Ebrahimi, C. Antoine, G. Carlo, B. Yoshua, · 2015
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S. Zagoruyko, N. Komodakis, · 2016
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Residual convolutional ctc networks for automatic speech recognition,
Y. Wang, X. Deng, S. Pu, Z. Huang, · 2017
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Attention is all you need,
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, · 2017
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Channel pruning for accelerating very deep neural networks,
Y. He, X. Zhang, J. Sun, · 2017
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Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer,
N. Komodakis, S. Zagoruyko, · 2017
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,
J. Yim, D. Joo, J. Bae, J. Kim, · 2017
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Neural trojans,
Y. Liu, Y. Xie, A. Srivastava, · 2017
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Two bi-objective hybrid approaches for the frequent subgraph mining problem,
S. H. Farhi, D. Boughaci, · 2018
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Amc: Automl for model compression and acceleration on mobile devices,
Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, S. Han, · 2018
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Kdgan: Knowledge distillation with generative adversarial networks,
X. Wang, R. Zhang, Y. Sun, J. Qi, · 2018
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Deep mutual learning,
Y. Zhang, T. Xiang, T. M. Hospedales, H. Lu, · 2018
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Self-supervised knowledge distillation using singular value decomposition,
S. H. Lee, D. H. Kim, B. C. Song, · 2018
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Spectral signatures in backdoor attacks,
B. Tran, J. Li, A. Madry, · 2018
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Fine-pruning: Defending against backdooring attacks on deep neural networks,
K. Liu, B. Dolan-Gavitt, S. Garg, · 2018
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Recent advances in computer vision,
M. Hassaballah, K. M. Hosny, · 2019
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Differentiable soft quantization: Bridging full-precision and low-bit neural networks,
R. Gong, X. Liu, S. Jiang, T. Li, P. Hu, J. Lin, F. Yu, J. Yan, · 2019
Cited alongside, same era.
Distillation-based training for multi-exit architectures,
M. Phuong, C. H. Lampert, · 2019
Cited alongside, same era.
Hidden trigger backdoor attacks,
A. Saha, A. Subramanya, H. Pirsiavash, · 2020
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Universal litmus patterns: Revealing backdoor attacks in cnns,
S. Kolouri, A. Saha, H. Pirsiavash, H. Hoffmann, · 2020
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Deep k-nn defense against clean-label data poisoning attacks,
N. Peri, N. Gupta, W. R. Huang, L. Fowl, C. Zhu, S. Feizi, T. Goldstein, J. P. Dickerson, · 2020
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Adversarially robust distillation,
M. Goldblum, L. Fowl, S. Feizi, T. Goldstein, · 2020
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From artificial neural networks to deep learning: A research survey,
Z. Zhang, · 2020
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A comprehensive survey of loss functions in machine learning,
Q. Wang, Y. Ma, K. Zhao, Y. Tian, · 2020
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On the efficacy of knowledge distillation,
J. H. Cho, B. Hariharan, · 2019
Cited alongside, same era.
Training deep neural networks in generations: A more tolerant teacher educates better students,
C. Yang, L. Xie, S. Qiao, A. L. Yuille, · 2019
Cited alongside, same era.
Relational knowledge distillation,
W. Park, D. Kim, Y. Lu, M. Cho, · 2019
Cited alongside, same era.
Similarity-preserving knowledge distillation,
F. Tung, G. Mori, · 2019
Cited alongside, same era.
Correlation congruence for knowledge distillation,
B. Peng, X. Jin, J. Liu, D. Li, Y. Wu, Y. Liu, S. Zhou, Z. Zhang, · 2019
Cited alongside, same era.
Knowledge transfer via distillation of activation boundaries formed by hidden neurons,
B. Heo, M. Lee, S. Yun, J. Y. Choi, · 2019
Cited alongside, same era.
Gpt-3: What’s it good for?,
R. Dale, · 2021
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Deep double descent: Where bigger models and more data hurt,
P. Nakkiran, G. Kaplun, Y. Bansal, T. Yang, B. Barak, I. Sutskever, · 2021
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Adversarial neuron pruning purifies backdoored deep models,
D. Wu, Y. Wang, · 2021
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Knowledge distillation: A survey,
J. Gou, B. Yu, S. J. Maybank, D. Tao, · 2021
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Drone: Data-aware low-rank compression for large nlp models,
P. Chen, H.-F. Yu, I. Dhillon, C.-J. Hsieh, · 2021
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Neural attention distillation: Erasing backdoor triggers from deep neural networks,
Y. Li, X. Lyu, N. Koren, L. Lyu, B. Li, X. Ma, · 2021
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Gpt-neox-20b: An open-source autoregressive language model,
S. Black, S. Biderman, E. Hallahan, Q. Anthony, L. Gao, L. Golding, H. He, C. Leahy, K. McDonell, J. Phang, et al., · 2022
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Decoupled knowledge distillation,
B. Zhao, Q. Cui, R. Song, Y. Qiu, J. Liang, · 2022
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Eliminating backdoor triggers for deep neural networks using attention relation graph distillation,
J. Xia, T. Wang, J. Ding, X. Wei, M. Chen, · 2022
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Dual discriminator adversarial distillation for data-free model compression,
H. Zhao, X. Sun, J. Dong, M. Manic, H. Zhou, H. Yu, · 2022
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S. M. Jain, Introduction to transformers for NLP: With the hugging face library and models to solve problems, Springer, 2022
2022
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Data augmentation techniques in natural language processing,
L. F. A. O. Pellicer, T. M. Ferreira, A. H. R. Costa, · 2023
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Learning to retain while acquiring: Combating distribution-shift in adversarial data-free knowledge distillation,
G. Patel, K. R. Mopuri, Q. Qiu, · 2023
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Backdoor cleansing with unlabeled data,
L. Pang, T. Sun, H. Ling, C. Chen, · 2023
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Latent backdoor attacks on deep neural networks,
Y. Yao, H. Li, H. Zheng, B. Y. Zhao, · 2055
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