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Knowledge Distillation (KD) has been used in image classification for model compression.
Semi-supervised self-training of object detection models
C. Rosenberg, M. Hebert, and H. Schneiderman · 2005
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2014
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Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Convolutional neural networks with low-rank regularization
C. Tai, T. Xiao, Y. Zhang, X. Wang, et al · 2015
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R-fcn: Object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Cited alongside, same era.
Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Cited alongside, same era.
Learning efficient object detection models with knowledge distillation
G. Chen, W. Choi, X. Yu, T. Han, and M. Chandraker · 2017
Cited alongside, same era.
Dssd: Deconvolutional single shot detector
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Data distillation: Towards omni-supervised learning
I. Radosavovic, P. Dollár, R. Girshick, G. Gkioxari, and K. He · 2017
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Yolo9000: better, faster, stronger
J. Redmon and A. Farhadi · 2017
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
J. Yim, D. Joo, J. Bae, and J. Kim · 2017
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Shufflenet: An extremely efficient convolutional neural network for mobile devices. arxiv 2017
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
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A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Mimicking very efficient network for object detection
Q. Li, S. Jin, and J. Yan · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2017
Cited alongside, same era.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Cited alongside, same era.
T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar · 2018
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Detectron
R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár, and K. He · 2018
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Quantization mimic: Towards very tiny cnn for object detection
Y. Wei, X. Pan, H. Qin, and J. Yan · 2018
Later among the works it cites.