Awesome Knowledge Distillation

Papers

Neural Network Ensembles, L.K. Hansen, P. Salamon, 1990
Neural Network Ensembles, Cross Validation, and Active Learning, Andres Krogh, Jesper Vedelsby, 1995
Combining labeled and unlabeled data with co-training, A. Blum, T. Mitchell, 1998
Ensemble Methods in Machine Learning, Thomas G. Dietterich, 2000
Model Compression, Rich Caruana, 2006
Dark knowledge, Geoffrey Hinton, Oriol Vinyals, Jeff Dean, 2014
Learning with Pseudo-Ensembles, Philip Bachman, Ouais Alsharif, Doina Precup, 2014
Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeff Dean, 2015
Cross Modal Distillation for Supervision Transfer, Saurabh Gupta, Judy Hoffman, Jitendra Malik, 2015
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization, Baohan Xu, Yanwei Fu, Yu-Gang Jiang, Boyang Li, Leonid Sigal, 2015
Distilling Model Knowledge, George Papamakarios, 2015
Unifying distillation and privileged information, David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik, 2015
Learning Using Privileged Information: Similarity Control and Knowledge Transfer, Vladimir Vapnik, Rauf Izmailov, 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks, Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami, 2016
Do deep convolutional nets really need to be deep and convolutional?, Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Ozlem Aslan, Shengjie Wang, Rich Caruana, Abdelrahman Mohamed, Matthai Philipose, Matt Richardson, 2016
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, Sergey Zagoruyko, Nikos Komodakis, 2016
FitNets: Hints for Thin Deep Nets, Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio, 2015
Deep Model Compression: Distilling Knowledge from Noisy Teachers, Bharat Bhusan Sau, Vineeth N. Balasubramanian, 2016
Knowledge Distillation for Small-footprint Highway Networks, Liang Lu, Michelle Guo, Steve Renals, 2016
Sequence-Level Knowledge Distillation, deeplearning-papernotes, Yoon Kim, Alexander M. Rush, 2016
MobileID: Face Model Compression by Distilling Knowledge from Neurons, Ping Luo, Zhenyao Zhu, Ziwei Liu, Xiaogang Wang and Xiaoou Tang, 2016
Recurrent Neural Network Training with Dark Knowledge Transfer, Zhiyuan Tang, Dong Wang, Zhiyong Zhang, 2016
Adapting Models to Signal Degradation using Distillation, Jong-Chyi Su, Subhransu Maji,2016
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, Antti Tarvainen, Harri Valpola, 2017
Data-Free Knowledge Distillation For Deep Neural Networks, Raphael Gontijo Lopes, Stefano Fenu, 2017
Like What You Like: Knowledge Distill via Neuron Selectivity Transfer, Zehao Huang, Naiyan Wang, 2017
Learning Loss for Knowledge Distillation with Conditional Adversarial Networks, Zheng Xu, Yen-Chang Hsu, Jiawei Huang, 2017
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang, 2017
Knowledge Projection for Deep Neural Networks, Zhi Zhang, Guanghan Ning, Zhihai He, 2017
Moonshine: Distilling with Cheap Convolutions, Elliot J. Crowley, Gavin Gray, Amos Storkey, 2017
Local Affine Approximators for Improving Knowledge Transfer, Suraj Srinivas and Francois Fleuret, 2017
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model, Jiasen Lu1, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra 2017
Learning Efficient Object Detection Models with Knowledge Distillation, Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, Manmohan Chandraker, 2017
Learning Transferable Architectures for Scalable Image Recognition, Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le, 2017
Revisiting knowledge transfer for training object class detectors, Jasper Uijlings, Stefan Popov, Vittorio Ferrari, 2017
A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning, Junho Yim, Donggyu Joo, Jihoon Bae, Junmo Kim, 2017
Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net, Zihao Liu, Qi Liu, Tao Liu, Yanzhi Wang, Wujie Wen, 2017
Data Distillation: Towards Omni-Supervised Learning, Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, Kaiming He, 2017
Parallel WaveNet:Fast High-Fidelity Speech Synthesis, Aaron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, 2017
Learning from Noisy Labels with Distillation, Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, Li-Jia Li, 2017
Deep Mutual Learning, Ying Zhang, Tao Xiang, Timothy M. Hospedales, Huchuan Lu, 2017
Distilling a Neural Network Into a Soft Decision Tree, Nicholas Frosst, Geoffrey Hinton, 2017
Interpreting Deep Classifiers by Visual Distillation of Dark Knowledge, Kai Xu, Dae Hoon Park, Chang Yi, Charles Sutton, 2018
Efficient Neural Architecture Search via Parameters Sharing, Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean, 2018
Defensive Collaborative Multi-task Training - Defending against Adversarial Attack towards Deep Neural Networks, Derek Wang, Chaoran Li, Sheng Wen, Yang Xiang, Wanlei Zhou, Surya Nepal, 2018
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation, Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou, 2018
Deep Co-Training for Semi-Supervised Image Recognition, Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, Alan Yuille, 2018
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples, Zihao Liu, Qi Liu, Tao Liu, Yanzhi Wang, Wujie Wen, 2018
Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling, Abrar H. Abdulnabi, Bing Shuai, Zhen Zuo, Lap-Pui Chau, Gang Wang, 2018
Born Again Neural Networks, Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar, 2018
YASENN: Explaining Neural Networks via Partitioning Activation Sequences, Yaroslav Zharov, Denis Korzhenkov, Pavel Shvechikov, Alexander Tuzhilin, 2018
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary, Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi, 2018
Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons, Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi, 2018
Self-supervised knowledge distillation using singular value decomposition, Seung Hyun Lee, Dae Ha Kim, Byung Cheol Song, 2018
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection, Yongcheng Liu, Lu Sheng, Jing Shao, Junjie Yan, Shiming Xiang, Chunhong Pan, 2018
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks, Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy, 2018
A Generalized Meta-loss function for regression and classification using privileged information, Amina Asif, Muhammad Dawood, Fayyaz ul Amir Afsar Minhas, 2018
Large scale distributed neural network training through online distillation, Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton, 2018
KDGAN: Knowledge Distillation with Generative Adversarial Networks, Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi, 2018
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation, Jayashree Karlekar, Jiashi Feng, Zi Sian Wong, Sugiri Pranata, 2019
Relational Knowledge Distillation, Wonpyo Park, Dongju Kim, Yan Lu, Minsu Cho, 2019
Graph-based Knowledge Distillation by Multi-head Attention Network, Seunghyun Lee, Byung Cheol Song, 2019
Knowledge Adaptation for Efficient Semantic Segmentation, Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, Youliang Yan, 2019
Structured Knowledge Distillation for Semantic Segmentation, Yifan Liu, Ke Chen, Chris Liu, Zengchang Qin, Zhenbo Luo, Jingdong Wang, 2019
Fast Human Pose Estimation, Feng Zhang, Xiatian Zhu, Mao Ye, 2019
MEAL: Multi-Model Ensemble via Adversarial Learning, Zhiqiang Shen, Zhankui He, Xiangyang Xue, 2019
Learning Lightweight Lane Detection CNNs by Self Attention Distillation, Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy, 2019
Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher, Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Hassan Ghasemzadeh, 2019
A Comprehensive Overhaul of Feature Distillation, Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, Jin Young Choi, 2019
Contrastive Representation Distillation, Yonglong Tian, Dilip Krishnan, Phillip Isola, 2019
Distillation-Based Training for Multi-Exit Architectures, Mary Phuong, Christoph H. Lampert, Am Campus, 2019
Learning Metrics from Teachers: Compact Networks for Image Embedding, Lu Yu, Vacit Oguz Yazici, Xialei Liu, Joost van de Weijer, Yongmei Cheng, Arnau Ramisa, 2019
On the Efficacy of Knowledge Distillation, Jang Hyun Cho, Bharath Hariharan, 2019
Revisit Knowledge Distillation: a Teacher-free Framework, Li Yuan, Francis E.H.Tay, Guilin Li, Tao Wang, Jiashi Feng, 2019
Ensemble Distribution Distillation, Andrey Malinin, Bruno Mlodozeniec, Mark Gales, 2019
Improving Generalization and Robustness with Noisy Collaboration in Knowledge Distillation, Elahe Arani, Fahad Sarfraz, Bahram Zonooz, 2019
Self-training with Noisy Student improves ImageNet classification, Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le, 2019
Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework, Srinidhi Hegde, Ranjitha Prasad, Ramya Hebbalaguppe, Vishwajith Kumar, 2019
Preparing Lessons: Improve Knowledge Distillation with Better Supervision, Tiancheng Wen, Shenqi Lai, Xueming Qian, 2019
Positive-Unlabeled Compression on the Cloud, Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing Xu, Dacheng Tao, Chang Xu, 2019
Variational Information Distillation for Knowledge Transfer, Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai, 2019
Knowledge Distillation via Instance Relationship Graph, Yufan Liu, Jiajiong Cao, Bing Li, Chunfeng Yuan, Weiming Hu, Yangxi Li and Yunqiang Duan, 2019
Knowledge Distillation via Route Constrained Optimization, Xiao Jin, Baoyun Peng, Yichao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Junjie Yan, Xiaolin Hu, 2019
Similarity-Preserving Knowledge Distillation, Frederick Tung, Greg Mori, 2019
Distilling Object Detectors with Fine-grained Feature Imitation, Tao Wang, Li Yuan, Xiaopeng Zhang, Jiashi Feng, 2019
Knowledge Squeezed Adversarial Network Compression, Shu Changyong, Li Peng, Xie Yuan, Qu Yanyun, Dai Longquan, Ma Lizhuang, 2019
Stagewise Knowledge Distillation, Akshay Kulkarni, Navid Panchi, Shital Chiddarwar, 2019
Knowledge Distillation from Internal Representations, Gustavo Aguilar, Yuan Ling, Yu Zhang, Benjamin Yao, Xing Fan, Edward Guo, 2019
Knowledge Flow: Improve Upon Your Teachers, Iou-Jen Liu, Jian Peng, Alexander G. Schwing, 2019
Graph Representation Learning via Multi-task Knowledge Distillation, Jiaqi Ma, Qiaozhu Mei, 2019
Deep geometric knowledge distillation with graphs, Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega, 2019
Correlation Congruence for Knowledge Distillation, Baoyun Peng, Xiao Jin, Jiaheng Liu, Shunfeng Zhou, Yichao Wu, Yu Liu, Dongsheng Li, Zhaoning Zhang, 2019
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation, Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, Kaisheng Ma, 2019
BAM! Born-Again Multi-Task Networks for Natural Language Understanding, Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, Quoc V. Le, 2019
Self-Knowledge Distillation in Natural Language Processing, Sangchul Hahn, Heeyoul Choi, 2019
Rethinking Data Augmentation: Self-Supervision and Self-Distillation, Hankook Lee, Sung Ju Hwang, Jinwoo Shin, 2019
MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks, Yunteng Luan, Hanyu Zhao, Zhi Yang, Yafei Dai, 2019
Efficient Video Classification Using Fewer Frames, Shweta Bhardwaj, Mukundhan Srinivasan, Mitesh M. Khapra, 2019
Retaining Privileged Information for Multi-Task Learning, Fengyi Tang, Cao Xiao, Fei Wang, Jiayu Zhou, Li-Wei Lehman
Data-Free Learning of Student Networks, Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang1, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, Qi Tian, 2019
Positive-Unlabeled Compression on the Cloud, Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing Xu, Dacheng Tao, Chang Xu, 2019
When Does Label Smoothing Help?, Rafael Müller, Simon Kornblith, Geoffrey Hinton, 2019
TinyBERT: Distilling BERT for Natural Language Understanding, Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu, 2019
The State of Knowledge Distillation for Classification, Fabian Ruffy, Karanbir Chahal, 2019
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks, Raphael Tang, Yao Lu, Linqing Liu, Lili Mou, Olga Vechtomova, Jimmy Lin, 2019
Channel Distillation: Channel-Wise Attention for Knowledge Distillation, Zaida Zhou, Chaoran Zhuge, Xinwei Guan, Wen Liu, 2020
Residual Knowledge Distillation, Mengya Gao, Yujun Shen, Quanquan Li, Chen Change Loy, 2020
ResKD: Residual-Guided Knowledge Distillation, Xuewei Li, Songyuan Li, Bourahla Omar, Fei Wu, Xi Li, 2020
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion, Hongxu Yin, Pavlo Molchanov, Zhizhong Li, Jose M. Alvarez, Arun Mallya, Derek Hoiem, Niraj K. Jha, Jan Kautz, 2020
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks, Zhiqiang Shen, Marios Savvides, 2020
MGD: Matching Guided Distillation, Kaiyu Yue, Jiangfan Deng, Feng Zhou, 2020
Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation, Jia Guo, Minghao Chen, Yao Hu, Chen Zhu, Xiaofei He, Deng Cai, 2020
Regularizing Class-wise Predictions via Self-knowledge Distillation, Sukmin Yun, Jongjin Park, Kimin Lee, Jinwoo Shin, 2020
Training data-efficient image transformers & distillation through attention, Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou, 2020
Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks, Lin Wang, Kuk-Jin Yoon, 2020
Cross-Layer Distillation with Semantic Calibration,Defang Chen, Jian-Ping Mei, Yuan Zhang, Can Wang, Yan Feng, Chun Chen, 2020
Subclass Distillation, Rafael Müller, Simon Kornblith, Geoffrey Hinton, 2020
MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis, Sergei Belousov, 2021
Knowledge Distillation: A Survey, Jianping Gou, Baosheng Yu, Stephen John Maybank, Dacheng Tao, 2021
Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation, Mingi Ji, Seungjae Shin, Seunghyun Hwang, Gibeom Park, Il-Chul Moon, 2021
Complementary Relation Contrastive Distillation,Jinguo Zhu, Shixiang Tang, Dapeng Chen, Shijie Yu, Yakun Liu, Aijun Yang, Mingzhe Rong, Xiaohua Wang, 2021
Distilling Knowledge via Knowledge Review,Pengguang Chen, Shu Liu, Hengshuang Zhao, Jiaya Jia, 2021
Hierarchical Self-supervised Augmented Knowledge Distillation, Chuanguang Yang, Zhulin An, Linhang Cai, Yongjun Xu, 2021
Causal Distillation for Language Models, Zhengxuan Wu, Atticus Geiger, Josh Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah D. Goodman, 2021
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting, Alessio Monti, Angelo Porrello, Simone Calderara, Pasquale Coscia, Lamberto Ballan, Rita Cucchiara, 2022
On Distillation of Guided Diffusion Models, Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans, 2022
Progressive Distillation for Fast Sampling of Diffusion Models, Tim Salimans, Jonathan Ho, 2022
Considerations When Learning Additive Explanations for Black-Box Models, Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana, 2023
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition, Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, Hoifung Poon, 2023
TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation, David Berthelot, Arnaud Autef, Jierui Lin, Dian Ang Yap, Shuangfei Zhai, Siyuan Hu, Daniel Zheng, Walter Talbott, Eric Gu, 2023
Adversarial Diffusion Distillation, Axel Sauer, Dominik Lorenz, Andreas Blattmann, Robin Rombach, 2023
One-step Diffusion with Distribution Matching Distillation, Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T. Freeman, Taesung Park, 2023
MobileSAMv2: Faster Segment Anything to Everything, Chaoning Zhang, Dongshen Han, Sheng Zheng, Jinwoo Choi, Tae-Ho Kim, Choong Seon Hong, 2023
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models, Raviteja Vemulapalli, Hadi Pouransari, Fartash Faghri, Sachin Mehta, Mehrdad Farajtabar, Mohammad Rastegari, Oncel Tuzel, 2023
Logit Standardization in Knowledge Distillation, Shangquan Sun, Wenqi Ren, Jingzhi Li, Rui Wang, Xiaochun Cao, 2024
VkD : Improving Knowledge Distillation using Orthogonal Projections, Roy Miles, Ismail Elezi, Jiankang Deng, 2024
Understanding the Role of the Projector in Knowledge Distillation, Roy Miles, Krystian Mikolajczyk, 2024
Information Theoretic Representation Distillation, Roy Miles, Adrian Lopez Rodriguez, Krystian Mikolajczyk, 2024
Precision Shaking and DORPO: Conceptual Foundations of LLM Knowledge Distillation Methods, Áron Cserveni, 2024
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation, Jonas Kohler, Albert Pumarola, Edgar Schönfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda, Ali Thabet, 2024
Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping, Jianbin Zheng, Minghui Hu, Zhongyi Fan, Chaoyue Wang, Changxing Ding, Dacheng Tao, Tat-Jen Cham, 2024
Generative Prompt Internalization, Haebin Shin, Lei Ji, Yeyun Gong, Sungdong Kim, Eunbi Choi, Minjoon Seo, 2024
DεpS: Delayed ε-Shrinking for Faster Once-For-All Training, Aditya Annavajjala, Alind Khare, Animesh Agrawal, Igor Fedorov, Hugo Latapie, Myungjin Lee, Alexey Tumanov, 2024
Transferring Knowledge from Large Foundation Models to Small Downstream Models, Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Yuyang Wang, Andrew Gordon Wilson, 2024
ScaleKD: Strong Vision Transformers Could Be Excellent Teachers, Jiawei Fan, Chao Li, Xiaolong Liu and Anbang Yao, 2024
EchoDFKD: Data-Free Knowledge Distillation for Cardiac Ultrasound Segmentation using Synthetic Data, Grégoire Petit, Nathan Palluau, Axel Bauer, Clemens Dlaska, 2025

Videos

Dark knowledge, Geoffrey Hinton, 2014
Model Compression, Rich Caruana, 2016

Implementations

MXNet

Bayesian Dark Knowledge

PyTorch

Lua

Example for teacher/student-based learning

Torch

Theano

Lasagne + Theano

Experiments-with-Distilling-Knowledge

Tensorflow

Caffe

Keras

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https://github.com/PaddlePaddle/PaddleSlim/tree/release/2.0.0/docs/zh_cn/tutorials