Knowledge-Distillation-Paper

This resposity maintains a collection of important papers on knowledge distillation.

• Knowledge-Distillation-Paper
  • Pioneering Papers
  • Survey Papers
  • Distillation Accelerates Diffusion Models
  • Feature Distillation
  • Online Knowledge Distillation
  • Multi-Teacher Knowledge Distillation
  • Data-Free Knowledge Distillation
  • Distillation for Segmentation
  • Useful Resources

Pioneering Papers

• Model Compression, KDD 2006

  • https://dl.acm.org/doi/abs/10.1145/1150402.1150464
  • Cristian Buciluǎ, Rich Caruana, Alexandru Niculescu-Mizil.

• Do Deep Nets Really Need to be Deep?, NeurIPS 2014

  • https://arxiv.org/abs/1312.6184
  • Lei Jimmy Ba, Rich Caruana.

• Distilling the Knowledge in a Neural Network, NeurIPS-workshop 2014

  • https://arxiv.org/abs/1503.02531
  • https://neurips.cc/virtual/2014/workshop/4294
  • Geoffrey Hinton, Oriol Vinyals, Jeff Dean.

Survey Papers

• Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks, TPAMI 2022

  • https://arxiv.org/abs/2004.05937
  • Lin Wang, Kuk-Jin Yoon.

• Knowledge Distillation: A Survey, IJCV 2021

  • https://arxiv.org/abs/2006.05525
  • Jianping Gou, Baosheng Yu, Stephen John Maybank, Dacheng Tao.

Distillation Accelerates Diffusion Models

Extremely Promising !!!!!

• Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed [Tensorflow]

  • https://arxiv.org/abs/2101.02388
  • Eric Luhman, Troy Luhman

• Progressive Distillation for Fast Sampling of Diffusion Models, ICLR 2022 [Tensorflow]

  • https://arxiv.org/abs/2202.00512
  • Tim Salimans, Jonathan Ho

• Accelerating Diffusion Sampling with Classifier-based Feature Distillation, ICME 2023 [PyTorch]

  • https://arxiv.org/abs/2211.12039
  • Wujie Sun, Defang Chen, Can Wang, Deshi Ye, Yan Feng, Chun Chen

• Fast Sampling of Diffusion Models via Operator Learning, ICML 2023 [PyTorch]

  • https://arxiv.org/abs/2211.13449
  • Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, Anima Anandkumar

• Consistency Models, ICML 2023 [PyTorch]

  • https://arxiv.org/abs/2303.01469
  • Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever

• TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation [PyTorch]

  • https://arxiv.org/abs/2303.04248
  • David Berthelot, Arnaud Autef, Jierui Lin, Dian Ang Yap, Shuangfei Zhai, Siyuan Hu, Daniel Zheng, Walter Talbott, Eric Gu

• A Geometric Perspective on Diffusion Models

  • https://arxiv.org/abs/2305.19947
  • Defang Chen, Zhenyu Zhou, Jian-Ping Mei, Chunhua Shen, Chun Chen, Can Wang

• BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping, ICML 2024 [PyTorch]

  • https://arxiv.org/abs/2306.05544
  • Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Lingjie Liu, Josh Susskind

• Fast ODE-based Sampling for Diffusion Models in Around 5 Steps, CVPR 2024 [PyTorch]

  • https://arxiv.org/abs/2312.00094
  • Zhenyu Zhou, Defang Chen, Can Wang, Chun Chen

• On the Trajectory Regularity of ODE-based Diffusion Sampling, ICML 2024 [PyTorch]

  • https://arxiv.org/abs/2405.11326
  • Defang Chen, Zhenyu Zhou, Can Wang, Chunhua Shen, Siwei Lyu

Feature Distillation

• FitNets: Hints for Thin Deep Nets, ICLR 2015 [Theano]

  • https://arxiv.org/abs/1412.6550
  • Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio.

• Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, ICLR 2017 [PyTorch]

  • https://arxiv.org/abs/1612.03928
  • Sergey Zagoruyko, Nikos Komodakis.

• Learning Deep Representations with Probabilistic Knowledge Transfer, ECCV 2018 [Pytorch]

  • https://arxiv.org/abs/1803.10837
  • Nikolaos Passalis, Anastasios Tefas.

• Relational Knowledge Distillation, CVPR 2019 [Pytorch]

  • https://arxiv.org/abs/1904.05068
  • Wonpyo Park, Dongju Kim, Yan Lu, Minsu Cho.

• Variational Information Distillation for Knowledge Transfer, CVPR 2019

  • https://arxiv.org/abs/1904.05835
  • Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai.

• Similarity-Preserving Knowledge Distillation, CVPR 2019

  • https://arxiv.org/abs/1907.09682
  • Frederick Tung, Greg Mori.

• Contrastive Representation Distillation, ICLR 2020 [Pytorch]

  • https://arxiv.org/abs/1910.10699
  • Yonglong Tian, Dilip Krishnan, Phillip Isola.

• Heterogeneous Knowledge Distillation using Information Flow Modeling, CVPR 2020 [Pytorch]

  • https://arxiv.org/abs/2005.00727
  • Nikolaos Passalis, Maria Tzelepi, Anastasios Tefas.

• Cross-Layer Distillation with Semantic Calibration, AAAI 2021 [Pytorch][TKDE]

  • https://arxiv.org/abs/2012.03236
  • Defang Chen, Jian-Ping Mei, Yuan Zhang, Can Wang, Zhe Wang, Yan Feng, Chun Chen.

• Distilling Knowledge via Knowledge Review, CVPR 2021 [Pytorch]

  • https://arxiv.org/abs/2104.09044
  • Pengguang Chen, Shu Liu, Hengshuang Zhao, Jiaya Jia.

• Distilling Holistic Knowledge with Graph Neural Networks, ICCV 2021 [Pytorch]

  • https://arxiv.org/abs/2108.05507
  • Sheng Zhou, Yucheng Wang, Defang Chen, Jiawei Chen, Xin Wang, Can Wang, Jiajun Bu.

• Decoupled Knowledge Distillation, CVPR 2022 [Pytorch]

  • https://arxiv.org/abs/2203.08679
  • Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, Jiajun Liang

• Knowledge Distillation with the Reused Teacher Classifier, CVPR 2022 [Pytorch]

  • https://arxiv.org/abs/2203.14001
  • Defang Chen, Jian-Ping Mei, Hailin Zhang, Can Wang, Yan Feng, Chun Chen.

Online Knowledge Distillation

• Deep Mutual Learning, CVPR 2018 [TensorFlow]

  • https://arxiv.org/abs/1706.00384
  • Ying Zhang, Tao Xiang, Timothy M. Hospedales, Huchuan Lu.

• Large scale distributed neural network training through online distillation, ICLR 2018

  • https://arxiv.org/abs/1804.03235
  • Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl and Geoffrey E. Hinton.

• Knowledge Distillation by On-the-Fly Native Ensemble, NeurIPS 2018 [PyTorch]

  • https://arxiv.org/abs/1806.04606
  • Xu Lan, Xiatian Zhu, Shaogang Gong.

• Online Knowledge Distillation with Diverse Peers, AAAI 2020 [Pytorch]

  • https://arxiv.org/abs/1912.00350
  • Defang Chen, Jian-Ping Mei, Can Wang, Yan Feng and Chun Chen.

• Feature-map-level Online Adversarial Knowledge Distillation, ICML 2020

  • https://arxiv.org/abs/2002.01775
  • Inseop Chung, SeongUk Park, Jangho Kim, Nojun Kwak.

• Peer collaborative learning for online knowledge distillation, AAAI 2021

  • https://arxiv.org/abs/2006.04147
  • Guile Wu, Shaogang Gong.

Multi-Teacher Knowledge Distillation

• Distilling knowledge from ensembles of neural networks for speech recognition, INTERSPEECH 2016

  • https://www.isca-speech.org/archive_v0/Interspeech_2016/pdfs/1190.PDF
  • Austin Waters, Yevgen Chebotar.

• Efficient Knowledge Distillation from an Ensemble of Teachers, INTERSPEECH 2017

  • https://isca-speech.org/archive_v0/Interspeech_2017/pdfs/0614.PDF
  • Takashi Fukuda, Masayuki Suzuki, Gakuto Kurata, Samuel Thomas, Jia Cui, Bhuvana Ramabhadran.

• Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space, NeurIPS 2020 [Pytorch]

  • https://proceedings.neurips.cc/paper/2020/hash/91c77393975889bd08f301c9e13a44b7-Abstract.html
  • Shangchen Du, Shan You, Xiaojie Li, Jianlong Wu, Fei Wang, Chen Qian, Changshui Zhang.

• Reinforced Multi-Teacher Selection for Knowledge Distillation, AAAI 2021

  • https://arxiv.org/abs/2012.06048
  • Fei Yuan, Linjun Shou, Jian Pei, Wutao Lin, Ming Gong, Yan Fu, Daxin Jiang

• Confidence-Aware Multi-Teacher Knowledge Distillation, ICASSP 2022 [Pytorch]

  • https://arxiv.org/abs/2201.00007
  • Hailin Zhang, Defang Chen, Can Wang.

• Adaptive Multi-Teacher Knowledge Distillation with Meta-Learning, ICME 2023 [Pytorch]

  • https://arxiv.org/abs/2306.06634
  • Hailin Zhang, Defang Chen, Can Wang.

Data-Free Knowledge Distillation

• Data-Free Knowledge Distillation for Deep Neural Networks, NeurIPS-workshop 2017 [Tensorflow]

  • https://arxiv.org/abs/1710.07535
  • Raphael Gontijo Lopes, Stefano Fenu, Thad Starner

• DAFL: Data-Free Learning of Student Networks, ICCV 2019 [PyTorch]

  • https://arxiv.org/abs/1904.01186
  • Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, Qi Tian

• Zero-Shot Knowledge Distillation in Deep Networks, ICML 2019 [Tensorflow]

  • https://arxiv.org/abs/1905.08114
  • Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, R. Venkatesh Babu, Anirban Chakraborty

• Zero-shot Knowledge Transfer via Adversarial Belief Matching, NeurIPS 2019 [Pytorch]

  • https://arxiv.org/abs/1905.09768
  • Paul Micaelli, Amos Storkey

• Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion, CVPR 2020 [Pytorch]

  • https://arxiv.org/abs/1912.08795
  • Hongxu Yin, Pavlo Molchanov, Zhizhong Li, Jose M. Alvarez, Arun Mallya, Derek Hoiem, Niraj K. Jha, Jan Kautz

• The Knowledge Within: Methods for Data-Free Model Compression, CVPR 2020

  • https://arxiv.org/abs/1912.01274
  • Matan Haroush, Itay Hubara, Elad Hoffer, Daniel Soudry

• Contrastive Model Inversion for Data-Free Knowledge Distillation, IJCAI 2021 [Pytorch]

  • https://arxiv.org/abs/2105.08584
  • Gongfan Fang, Jie Song, Xinchao Wang, Chengchao Shen, Xingen Wang, Mingli Song

• Customizing Synthetic Data for Data-Free Student Learning, ICME 2023 [Pytorch]

  • https://arxiv.org/abs/2307.04542
  • Shiya Luo, Defang Chen, Can Wang

Distillation for Segmentation

• Structured Knowledge Distillation for Dense Prediction, CVPR 2019, TPAMI 2020 [Pytorch]

  • https://arxiv.org/abs/1903.04197
  • Yifan Liu, Changyong Shun, Jingdong Wang, Chunhua Shen.

• Channel-wise Knowledge Distillation for Dense Prediction, ICCV 2021 [Pytorch]

  • https://arxiv.org/abs/2011.13256
  • Changyong Shu, Yifan Liu, Jianfei Gao, Zheng Yan, Chunhua Shen.

• Cross-Image Relational Knowledge Distillation for Semantic Segmentation, CVPR 2022 [Pytorch]

  • https://arxiv.org/abs/2204.06986
  • Chuanguang Yang, Helong Zhou, Zhulin An, Xue Jiang, Yongjun Xu, Qian Zhang.

• Holistic Weighted Distillation for Semantic Segmentation, ICME 2023 [Pytorch]

  • Wujie Sun, Defang Chen, Can Wang, Deshi Ye, Yan Feng, Chun Chen.

Useful Resources

• Acceptance rates of the main AI conferences [Link]
• AI conference deadlines [Link]
• CCF conference deadlines [Link]