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https://github.com/eric-erki/awesome-knowledge-distillation

Awesome Knowledge Distillation
https://github.com/eric-erki/awesome-knowledge-distillation

List: awesome-knowledge-distillation

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Awesome Knowledge Distillation

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# Awesome Knowledge Distillation

================================



### Papers

- [Neural Network Ensembles](https://www.researchgate.net/publication/3191841_Neural_Network_Ensembles), L.K. Hansen, P. Salamon, 1990

- [Neural Network Ensembles, Cross Validation, and Active Learning](https://papers.nips.cc/paper/1001-neural-network-ensembles-cross-validation-and-active-learning.pdf), Andres Krogh, Jesper Vedelsby, 1995

- [Combining labeled and unlabeled data with co-training](https://www.cs.cmu.edu/~avrim/Papers/cotrain.pdf), A. Blum, T. Mitchell, 1998

- [Ensemble Methods in Machine Learning](http://web.engr.oregonstate.edu/~tgd/publications/mcs-ensembles.pdf), Thomas G. Dietterich, 2000

- [Model Compression](http://www.cs.cornell.edu/~caruana/compression.kdd06.pdf), Rich Caruana, 2006

- [Dark knowledge](http://www.ttic.edu/dl/dark14.pdf), Geoffrey Hinton , OriolVinyals & Jeff Dean, 2014

- [Learning with Pseudo-Ensembles](https://arxiv.org/pdf/1412.4864.pdf), Philip Bachman, Ouais Alsharif, Doina Precup, 2014

- [Distilling the Knowledge in a Neural Network](https://arxiv.org/pdf/1503.02531.pdf), Hinton, J.Dean, 2015

- [Cross Modal Distillation for Supervision Transfer](https://arxiv.org/pdf/1507.00448), Saurabh Gupta, Judy Hoffman, Jitendra Malik, 2015

- [Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization](https://arxiv.org/pdf/1511.04798), Baohan Xu, Yanwei Fu, Yu-Gang Jiang, Boyang Li, Leonid Sigal, 2015

- [Distilling Model Knowledge](https://arxiv.org/pdf/1510.02437.pdf), George Papamakarios, 2015

- [Unifying distillation and privileged information](https://arxiv.org/pdf/1511.03643), David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik, 2015

- [Learning Using Privileged Information: Similarity Control and Knowledge Transfer](http://www.jmlr.org/papers/volume16/vapnik15b/vapnik15b.pdf), Vladimir Vapnik, Rauf Izmailov, 2015

- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://arxiv.org/pdf/1511.04508.pdf), Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami, 2016

- [Do deep convolutional nets really need to be deep and convolutional?](https://arxiv.org/pdf/1603.05691.pdf), 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](https://arxiv.org/pdf/1612.03928), Sergey Zagoruyko, Nikos Komodakis, 2016

- [FitNets: Hints for Thin Deep Nets](https://arxiv.org/pdf/1412.6550), Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio, 2015

- [Deep Model Compression: Distilling Knowledge from Noisy Teachers](https://arxiv.org/pdf/1610.09650), Bharat Bhusan Sau, Vineeth N. Balasubramanian, 2016

- [Knowledge Distillation for Small-footprint Highway Networks](https://arxiv.org/pdf/1608.00892), Liang Lu, Michelle Guo, Steve Renals, 2016

- [Sequence-Level Knowledge Distillation](https://arxiv.org/pdf/1606.07947), [deeplearning-papernotes](https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/seq-knowledge-distillation.md), Yoon Kim, Alexander M. Rush, 2016

- [MobileID: Face Model Compression by Distilling Knowledge from Neurons](http://personal.ie.cuhk.edu.hk/~pluo/pdf/aaai16-face-model-compression.pdf), Ping Luo,  Zhenyao Zhu, Ziwei Liu, Xiaogang Wang and Xiaoou Tang, 2016

- [Recurrent Neural Network Training with Dark Knowledge Transfer](https://arxiv.org/pdf/1505.04630.pdf), Zhiyuan Tang, Dong Wang, Zhiyong Zhang, 2016

- [Adapting Models to Signal Degradation using Distillation](https://arxiv.org/abs/1604.00433), Jong-Chyi Su, Subhransu Maji,2016

- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://arxiv.org/pdf/1703.01780), Antti Tarvainen, Harri Valpola, 2017

- [Data-Free Knowledge Distillation For Deep Neural Networks](http://raphagl.com/research/replayed-distillation/), Raphael Gontijo Lopes, Stefano Fenu, 2017 

- [Like What You Like: Knowledge Distill via Neuron Selectivity Transfer](https://arxiv.org/pdf/1707.01219), Zehao Huang, Naiyan Wang, 2017

- [Learning Loss for Knowledge Distillation with Conditional Adversarial Networks](https://arxiv.org/pdf/1709.00513), Zheng Xu, Yen-Chang Hsu, Jiawei Huang, 2017

- [DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer](https://arxiv.org/pdf/1707.01220), Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang, 2017

- [Knowledge Projection for Deep Neural Networks](https://arxiv.org/pdf/1710.09505), Zhi Zhang, Guanghan Ning, Zhihai He, 2017

- [Moonshine: Distilling with Cheap Convolutions](https://arxiv.org/pdf/1711.02613), Elliot J. Crowley, Gavin Gray, Amos Storkey, 2017

- [Local Affine Approximators for Improving Knowledge Transfer](https://lld-workshop.github.io/papers/LLD_2017_paper_28.pdf), Suraj Srinivas and Francois Fleuret, 2017

- [Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model](http://papers.nips.cc/paper/6635-best-of-both-worlds-transferring-knowledge-from-discriminative-learning-to-a-generative-visual-dialog-model.pdf), Jiasen Lu1, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra 2017

- [Learning Efficient Object Detection Models with Knowledge Distillation](http://papers.nips.cc/paper/6676-learning-efficient-object-detection-models-with-knowledge-distillation.pdf), Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, Manmohan Chandraker, 2017

- [Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification](https://arxiv.org/pdf/1709.02929), Chong Wang, Xipeng Lan and Yangang Zhang, 2017

- [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/pdf/1707.07012.pdf), Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le, 2017

- [Revisiting knowledge transfer for training object class detectors](https://arxiv.org/pdf/1708.06128.pdf), Jasper Uijlings, Stefan Popov, Vittorio Ferrari, 2017

- [A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yim_A_Gift_From_CVPR_2017_paper.pdf), Junho Yim, Donggyu Joo, Jihoon Bae, Junmo Kim, 2017

- [Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net](https://arxiv.org/pdf/1708.04106.pdf), Zihao Liu, Qi Liu, Tao Liu, Yanzhi Wang, Wujie Wen, 2017

- [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/pdf/1712.04440.pdf), Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, Kaiming He, 2017

- [Learning from Noisy Labels with Distillation](https://arxiv.org/abs/1703.02391), Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, Li-Jia Li, 2017 

- [Interpreting Deep Classifiers by Visual Distillation of Dark Knowledge](https://arxiv.org/pdf/1803.04042.pdf), Kai Xu, Dae Hoon Park, Chang Yi, Charles Sutton, 2018

- [Efficient Neural Architecture Search via Parameters Sharing](https://arxiv.org/pdf/1802.03268), Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean, 2018

- [Transparent Model Distillation](https://arxiv.org/pdf/1801.08640.pdf), Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo, 2018

- [Defensive Collaborative Multi-task Training - Defending against Adversarial Attack towards Deep Neural Networks](https://arxiv.org/pdf/1803.05123), Derek Wang, Chaoran Li, Sheng Wen, Yang Xiang, Wanlei Zhou, Surya Nepal, 2018 

- [Deep Co-Training for Semi-Supervised Image Recognition](https://arxiv.org/pdf/1803.05984), Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, Alan Yuille, 2018

- [Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples](https://arxiv.org/pdf/1803.05787), Zihao Liu, Qi Liu, Tao Liu, Yanzhi Wang, Wujie Wen, 2018

- [Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling](https://arxiv.org/pdf/1803.04687.pdf), Abrar H. Abdulnabi, Bing Shuai, Zhen Zuo, Lap-Pui Chau, Gang Wang, 2018

- [Born Again Neural Networks](https://arxiv.org/abs/1805.04770), Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar, 2018

- [YASENN: Explaining Neural Networks via Partitioning Activation Sequences](https://arxiv.org/abs/1811.02783), Yaroslav Zharov, Denis Korzhenkov, Pavel Shvechikov, Alexander Tuzhilin, 2018

- [Knowledge Distillation with Adversarial Samples Supporting Decision Boundary](https://arxiv.org/abs/1805.05532), Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi, 2018

- [Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons](https://arxiv.org/abs/1811.03233), Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi, 2018

- [Self-supervised knowledge distillation using singular value decomposition](http://openaccess.thecvf.com/content_ECCV_2018/html/SEUNG_HYUN_LEE_Self-supervised_Knowledge_Distillation_ECCV_2018_paper.html), Seung Hyun Lee, Dae Ha Kim, Byung Cheol Song, 2018

- [Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection](https://arxiv.org/abs/1809.05884), Yongcheng Liu, Lu Sheng, Jing Shao, Junjie Yan, Shiming Xiang, Chunhong Pan, 2018

- [Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks](https://arxiv.org/abs/1811.02759), Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy, 2018

- [A Generalized Meta-loss function for regression and classification using privileged information](https://arxiv.org/abs/1811.06885), Amina Asif, Muhammad Dawood, Fayyaz ul Amir Afsar Minhas, 2018

- [Large scale distributed neural network training through online distillation](https://arxiv.org/abs/1804.03235), Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton, 2018

- [KDGAN: Knowledge Distillation with Generative Adversarial Networks](http://papers.nips.cc/paper/7358-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](https://arxiv.org/abs/1906.00619), Jayashree Karlekar, Jiashi Feng, Zi Sian Wong, Sugiri Pranata, 2019

- [Relational Knowledge Distillation](https://arxiv.org/abs/1904.05068), Wonpyo Park, Dongju Kim, Yan Lu, Minsu Cho, 2019

- [Graph-based Knowledge Distillation by Multi-head Attention Network](https://arxiv.org/abs/1907.02226), Seunghyun Lee, Byung Cheol Song, 2019

- [Knowledge Adaptation for Efficient Semantic Segmentation](https://arxiv.org/abs/1903.04688), Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, Youliang Yan, 2019

- [Structured Knowledge Distillation for Semantic Segmentation](https://arxiv.org/abs/1903.04197), Yifan Liu, Ke Chen, Chris Liu, Zengchang Qin, Zhenbo Luo, Jingdong Wang, 2019

- [Fast Human Pose Estimation](https://arxiv.org/abs/1811.05419), Feng Zhang, Xiatian Zhu, Mao Ye, 2019

- [MEAL: Multi-Model Ensemble via Adversarial Learning](https://arxiv.org/abs/1812.02425), Zhiqiang Shen, Zhankui He, Xiangyang Xue, 2019

- [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://arxiv.org/abs/1908.00821), Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy, 2019

- [Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher](https://arxiv.org/abs/1902.03393), Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Hassan Ghasemzadeh, 2019

- [A Comprehensive Overhaul of Feature Distillation](https://arxiv.org/abs/1904.01866), Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, Jin Young Choi, 2019

- [Contrastive Representation Distillation](https://arxiv.org/pdf/1910.10699.pdf), Yonglong Tian, Dilip Krishnan, Phillip Isola, 2019

- [Distillation-Based Training for Multi-Exit Architectures](http://openaccess.thecvf.com/content_ICCV_2019/papers/Phuong_Distillation-Based_Training_for_Multi-Exit_Architectures_ICCV_2019_paper.pdf), Mary Phuong, Christoph H. Lampert, Am Campus, 2019

- [Learning Metrics from Teachers: Compact Networks for Image Embedding](https://arxiv.org/abs/1904.03624), Lu Yu, Vacit Oguz Yazici, Xialei Liu, Joost van de Weijer, Yongmei Cheng, Arnau Ramisa, 2019

- [On the Efficacy of Knowledge Distillation](https://arxiv.org/abs/1910.01348), Jang Hyun Cho, Bharath Hariharan, 2019

- [Revisit Knowledge Distillation: a Teacher-free Framework](https://arxiv.org/abs/1909.11723), Li Yuan, Francis E.H.Tay, Guilin Li, Tao Wang, Jiashi Feng, 2019

- [Ensemble Distribution Distillation](https://arxiv.org/abs/1905.00076), Andrey Malinin, Bruno Mlodozeniec, Mark Gales, 2019

- [Improving Generalization and Robustness with Noisy Collaboration in Knowledge Distillation](https://arxiv.org/abs/1910.05057), Elahe Arani, Fahad Sarfraz, Bahram Zonooz, 2019

- [Self-training with Noisy Student improves ImageNet classification](https://arxiv.org/abs/1911.04252), Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le, 2019

- [Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework](https://arxiv.org/abs/1910.12061), Srinidhi Hegde, Ranjitha Prasad, Ramya Hebbalaguppe, Vishwajith Kumar, 2019

- [Preparing Lessons: Improve Knowledge Distillation with Better Supervision](https://arxiv.org/abs/1911.07471), Tiancheng Wen, Shenqi Lai, Xueming Qian, 2019

- [Positive-Unlabeled Compression on the Cloud](https://arxiv.org/abs/1909.09757), Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing Xu, Dacheng Tao, Chang Xu, 2019

- [Variational Information Distillation for Knowledge Transfer](https://arxiv.org/abs/1904.05835), Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai, 2019

- [Knowledge Distillation via Instance Relationship Graph](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Knowledge_Distillation_via_Instance_Relationship_Graph_CVPR_2019_paper.pdf), Yufan Liu, Jiajiong Cao, Bing Li, Chunfeng Yuan, Weiming Hu, Yangxi Li and Yunqiang Duan, 2019

- [Knowledge Distillation via Route Constrained Optimization](https://arxiv.org/abs/1904.09149), Xiao Jin, Baoyun Peng, Yichao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Junjie Yan, Xiaolin Hu, 2019

- [Similarity-Preserving Knowledge Distillation](https://arxiv.org/abs/1907.09682), Frederick Tung, Greg Mori, 2019

- [Distilling Object Detectors with Fine-grained Feature Imitation](https://arxiv.org/abs/1906.03609), Tao Wang, Li Yuan, Xiaopeng Zhang, Jiashi Feng, 2019

- [Knowledge Squeezed Adversarial Network Compression](https://arxiv.org/abs/1904.05100), Shu Changyong, Li Peng, Xie Yuan, Qu Yanyun, Dai Longquan, Ma Lizhuang, 2019

- [Stagewise Knowledge Distillation](https://arxiv.org/abs/1911.06786), Akshay Kulkarni, Navid Panchi, Shital Chiddarwar, 2019

- [Knowledge Distillation from Internal Representations](https://arxiv.org/abs/1910.03723), Gustavo Aguilar, Yuan Ling, Yu Zhang, Benjamin Yao, Xing Fan, Edward Guo, 2019

- [Knowledge Flow: Improve Upon Your Teachers](https://arxiv.org/abs/1904.05878), Iou-Jen Liu, Jian Peng, Alexander G. Schwing, 2019

- [Graph Representation Learning via Multi-task Knowledge Distillation](https://arxiv.org/abs/1911.05700), Jiaqi Ma, Qiaozhu Mei, 2019

- [Deep geometric knowledge distillation with graphs](https://arxiv.org/abs/1911.03080), Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega, 2019

- [Correlation Congruence for Knowledge Distillation](https://arxiv.org/abs/1904.01802), 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](https://arxiv.org/abs/1905.08094), Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, Kaisheng Ma, 2019 

- [BAM! Born-Again Multi-Task Networks for Natural Language Understanding](https://arxiv.org/abs/1907.04829), Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, Quoc V. Le, 2019

- [Self-Knowledge Distillation in Natural Language Processing](https://arxiv.org/abs/1908.01851), Sangchul Hahn, Heeyoul Choi, 2019

- [Rethinking Data Augmentation: Self-Supervision and Self-Distillation](https://arxiv.org/abs/1910.05872), Hankook Lee, Sung Ju Hwang, Jinwoo Shin, 2019 

- [MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks](https://arxiv.org/abs/1911.09418), Yunteng Luan, Hanyu Zhao, Zhi Yang, Yafei Dai, 2019

- [Efficient Video Classification Using Fewer Frames](https://arxiv.org/abs/1902.10640), Shweta Bhardwaj, Mukundhan Srinivasan, Mitesh M. Khapra, 2019

- [Retaining Privileged Information for Multi-Task Learning](http://web.mit.edu/lilehman/www/paper/LUPI_KDD2019.pdf), Fengyi Tang, Cao Xiao, Fei Wang, Jiayu Zhou, Li-Wei Lehman

- [Data-Free Learning of Student Networks](https://arxiv.org/pdf/1904.01186.pdf), 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](https://arxiv.org/pdf/1909.09757.pdf), Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing Xu, Dacheng Tao, Chang Xu,2019

***

### Videos

- [Dark knowledge](https://www.youtube.com/watch?v=EK61htlw8hY), Geoffrey Hinton, 2014

- [Model Compression](https://www.youtube.com/watch?v=0WZmuryQdgg), Rich Caruana, 2016

***

### Implementations



## MXNet

- [Bayesian Dark Knowledge](https://github.com/dmlc/mxnet/blob/master/example/bayesian-methods/bdk.ipynb)



## PyTorch

- [Attention Transfer](https://github.com/szagoruyko/attention-transfer)

- [Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model](https://github.com/jiasenlu/visDial.pytorch)

- [Interpreting Deep Classifier by Visual Distillation of Dark Knowledge](https://github.com/xukai92/darksight)

- [A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility](https://github.com/peterliht/knowledge-distillation-pytorch)

- [Mean teachers are better role models](https://github.com/CuriousAI/mean-teacher)

- [Neural Network Distiller by Intel AI Lab](https://github.com/NervanaSystems/distiller), [distiller/knowledge_distillation.py](https://github.com/NervanaSystems/distiller/blob/master/distiller/knowledge_distillation.py)

- [Relational Knowledge Distillation](https://github.com/lenscloth/RKD)

- [Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons](https://github.com/bhheo/AB_distillation)

- [Fast Human Pose Estimation Pytorch](https://github.com/yuanyuanli85/Fast_Human_Pose_Estimation_Pytorch)

- [MEAL: Multi-Model Ensemble via Adversarial Learning](https://github.com/AaronHeee/MEAL)

- [Using Teacher Assistants to Improve Knowledge Distillation](https://github.com/imirzadeh/Teacher-Assistant-Knowledge-Distillation)

- [A Comprehensive Overhaul of Feature Distillation](https://github.com/clovaai/overhaul-distillation)

- [Contrastive Representation Distillation](https://github.com/HobbitLong/RepDistiller)

- [Transformer model distillation](http://nlp_architect.nervanasys.com/transformers_distillation.html)

- [TinyBERT](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT)

- [Data Efficient Model Compression ](https://github.com/huawei-noah/Data-Efficient-Model-Compression)



## Lua

- [Example for teacher/student-based learning ](https://github.com/hoelzl/Academia)



## Torch

- [Distilling knowledge to specialist ConvNets for clustered classification ](https://github.com/natoromano/specialistnets)

- [Sequence-Level Knowledge Distillation](https://github.com/harvardnlp/seq2seq-attn), [Neural Machine Translation on Android](https://github.com/harvardnlp/nmt-android)

- [cifar.torch distillation](https://github.com/samirasamadi/Distillation)

- [ENet-SAD](https://github.com/cardwing/Codes-for-Lane-Detection)



## Theano

- [FitNets: Hints for Thin Deep Nets](https://github.com/net-titech/distillation/tree/master/FitNets)

- [Transfer knowledge from a large DNN or an ensemble of DNNs into a small DNN](https://github.com/tejasgodambe/knowledge-distillation)



## Lasagne + Theano

- [Experiments-with-Distilling-Knowledge](https://github.com/usholanb/Experiments-with-Distilling-Knowledge)



## Tensorflow

- [Deep Model Compression: Distilling Knowledge from Noisy Teachers](https://github.com/chengshengchan/model_compression)

- [Distillation](https://github.com/suhangpro/distillation)

- [An example application of neural network distillation to MNIST](https://github.com/akamaus/mnist-distill)

- [Data-free Knowledge Distillation for Deep Neural Networks](https://github.com/iRapha/replayed_distillation)

- [Inspired by net2net, network distillation ](https://github.com/luzai/NetworkCompress)

- [Deep Reinforcement Learning, knowledge transfer](https://github.com/arnomoonens/DeepRL/tree/master/agents/knowledgetransfer)

- [Knowledge Distillation using Tensorflow](https://github.com/DushyantaDhyani/kdtf)

- [Knowledge Distillation Methods with Tensorflow](https://github.com/sseung0703/KD_methods_with_TF)

- [Zero-Shot Knowledge Distillation in Deep Networks in ICML2019 ](https://github.com/sseung0703/Zero-shot_Knowledge_Distillation)

- [Knowledge_distillation_benchmark via Tensorflow2.0](https://github.com/sseung0703/Knowledge_distillation_via_TF2.0)



## Caffe

- [Face Model Compression by Distilling Knowledge from Neurons](https://github.com/liuziwei7/mobile-id)

- [KnowledgeDistillation Layer (Caffe implementation)](https://github.com/wentianli/knowledge_distillation_caffe)

- [Knowledge distillation, realized in caffe ](https://github.com/smn2010/caffe_kdistill)

- [Cross Modal Distillation for Supervision Transfer](https://github.com/xiaolonw/fast-rcnn-distillation)

- [Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection](https://github.com/Yochengliu/MLIC-KD-WSD)

- [Knowledge Distillation via Instance Relationship Graph](https://github.com/yufanLIU/IRG)



## Keras

- [Knowledge distillation with Keras](https://github.com/TropComplique/knowledge-distillation-keras)

- [keras google-vision's distillation ](https://github.com/GINK03/keras-distillation)

- [Distilling the knowledge in a Neural Network](https://github.com/tejasgodambe/knowledge-distillation)