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Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student.
Image quality assessment: from error visibility to structural similarity
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
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Model compression
C. Bucilǎ, R. Caruana, and A. Niculescu-Mizil · 2006
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Information content weighting for perceptual image quality assessment
Z. Wang and Q. Li · 2010
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FSIM: A feature similarity index for image quality assessment
L. Zhang, L. Zhang, X. Mou, and D. Zhang · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Predicting parameters in deep learning
M. Denil, B. Shakibi, L. Dinh, M. Ranzato, and N. De Freitas · 2013
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Gradient magnitude similarity deviation: A highly efficient perceptual image quality index
W. Xue, L. Zhang, X. Mou, and A. C. Bovik · 2013
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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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Learning both weights and connections for efficient neural networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
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Distilling the Knowledge in a Neural Network
G. Hinton, O. Vinyals, and J. Dean · 2015
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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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Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
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Learning efficient object detection models with knowledge distillation
G. Chen, W. Choi, X. Yu, T. Han, and M. Chandraker · 2017
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Speed/accuracy trade-offs for modern convolutional object detectors
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and others · 2017
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Mimicking very efficient network for object detection
Q. Li, S. Jin, and J. Yan · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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Distilling object detectors with fine-grained feature imitation
T. Wang, L. Yuan, X. Zhang, and J. Feng · 2019
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Detectron2, 2019
Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick · 2019
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Reppoints: Point set representation for object detection
Z. Yang, S. Liu, H. Hu, L. Wang, and S. Lin · 2019
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Feature selective anchor-free module for single-shot object detection
C. Zhu, Y. He, and M. Savvides · 2019
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Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
E. Riba, D. Mishkin, D. Ponsa, E. Rublee, and G. Bradski · 2020
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General Instance Distillation for Object Detection
X. Dai, Z. Jiang, Z. Wu, Y. Bao, Z. Wang, S. Liu, and E. Zhou · 2021
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
Cited alongside, same era.
Cascade R-CNN: high quality object detection and instance segmentation
Z. Cai and N. Vasconcelos · 2018
Cited alongside, same era.
Non-local neural networks
X. Wang, R. Girshick, A. Gupta, and K. He · 2018
Cited alongside, same era.
MMDetection: Open MMLab detection toolbox and benchmark
K. Chen, J. Wang, J. Pang, Y. Cao, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Xu, Z. Zhang, D. Cheng, C. Zhu, T. Cheng, Q. Zhao, B. Li, X. Lu, R. Zhu, Y. Wu, J. Dai, J. Wang, J. Shi, W. Ouyang, C. C. Loy, and D. Lin · 2019
Cited alongside, same era.
Structured knowledge distillation for semantic segmentation
Y. Liu, K. Chen, C. Liu, Z. Qin, Z. Luo, and J. Wang · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Cited alongside, same era.
Distilling object detectors via decoupled features
J. Guo, K. Han, Y. Wang, H. Wu, X. Chen, C. Xu, and C. Xu · 2021
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Instance-Conditional Knowledge Distillation for Object Detection
Z. Kang, P. Zhang, X. Zhang, J. Sun, and N. Zheng · 2021
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Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks
L. Wang and K. J. Yoon · 2021
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Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient Detectors
L. Zhang and K. Ma · 2021
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Distilling Object Detectors with Feature Richness
D. Zhixing, R. Zhang, M. Chang, S. Liu, T. Chen, Y. Chen, and others · 2021
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