Awesome Knowledge-Distillation

Awesome Knowledge-Distillation

Different forms of knowledge

Knowledge from logits

Distilling the knowledge in a neural network. Hinton et al. arXiv:1503.02531
Learning from Noisy Labels with Distillation. Li, Yuncheng et al. ICCV 2017
Training Deep Neural Networks in Generations:A More Tolerant Teacher Educates Better Students. arXiv:1805.05551
Knowledge distillation by on-the-fly native ensemble. Lan, Xu et al. NIPS 2018
Learning Metrics from Teachers: Compact Networks for Image Embedding. Yu, Lu et al. CVPR 2019
Relational Knowledge Distillation. Park, Wonpyo et al, CVPR 2019
Like What You Like: Knowledge Distill via Neuron Selectivity Transfer. Huang, Zehao and Wang, Naiyan. 2017
On Knowledge Distillation from Complex Networks for Response Prediction. Arora, Siddhartha et al. NAACL 2019
On the Efficacy of Knowledge Distillation. Cho, Jang Hyun and Hariharan, Bharath. arXiv:1910.01348. ICCV 2019
Revisit Knowledge Distillation: a Teacher-free Framework(Revisiting Knowledge Distillation via Label Smoothing Regularization). Yuan, Li et al. CVPR 2020 [code]
Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher. Mirzadeh et al. arXiv:1902.03393
Ensemble Distribution Distillation. ICLR 2020
Noisy Collaboration in Knowledge Distillation. ICLR 2020
On Compressing U-net Using Knowledge Distillation. arXiv:1812.00249
Distillation-Based Training for Multi-Exit Architectures. Phuong, Mary and Lampert, Christoph H. ICCV 2019
Self-training with Noisy Student improves ImageNet classification. Xie, Qizhe et al.(Google) CVPR 2020
Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework. arXiv:1910.12061
Preparing Lessons: Improve Knowledge Distillation with Better Supervision. arXiv:1911.07471
Adaptive Regularization of Labels. arXiv:1908.05474
Positive-Unlabeled Compression on the Cloud. Xu, Yixing(HUAWEI) et al. NIPS 2019
Snapshot Distillation: Teacher-Student Optimization in One Generation. Yang, Chenglin et al. CVPR 2019
QUEST: Quantized embedding space for transferring knowledge. Jain, Himalaya et al. CVPR 2020(pre)
Conditional teacher-student learning. Z. Meng et al. ICASSP 2019
Subclass Distillation. Müller, Rafael et al. arXiv:2002.03936
MarginDistillation: distillation for margin-based softmax. Svitov, David & Alyamkin, Sergey. arXiv:2003.02586
An Embarrassingly Simple Approach for Knowledge Distillation. Gao, Mengya et al. MLR 2018
Sequence-Level Knowledge Distillation. Kim, Yoon & Rush, Alexander M. arXiv:1606.07947
Boosting Self-Supervised Learning via Knowledge Transfer. Noroozi, Mehdi et al. CVPR 2018
Meta Pseudo Labels. Pham, Hieu et al. ICML 2020
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model. CVPR 2020 [code]
Distilled Binary Neural Network for Monaural Speech Separation. Chen Xiuyi et al. IJCNN 2018
Teacher-Class Network: A Neural Network Compression Mechanism. Malik et al. arXiv:2004.03281
Deeply-supervised knowledge synergy. Sun, Dawei et al. CVPR 2019
What it Thinks is Important is Important: Robustness Transfers through Input Gradients. Chan, Alvin et al. CVPR 2020
Triplet Loss for Knowledge Distillation. Oki, Hideki et al. IJCNN 2020
Role-Wise Data Augmentation for Knowledge Distillation. ICLR 2020 [code]
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks. arXiv:2005.00288
Improved Noisy Student Training for Automatic Speech Recognition. Park et al.arXiv:2005.09629
Learning from a Lightweight Teacher for Efficient Knowledge Distillation. Yuang Liu et al. arXiv:2005.09163
ResKD: Residual-Guided Knowledge Distillation. Li, Xuewei et al. arXiv:2006.04719
Distilling Effective Supervision from Severe Label Noise. Zhang, Zizhao. et al. CVPR 2020 [code]
Knowledge Distillation Meets Self-Supervision. Xu, Guodong et al. ECCV 2020 [code]
Self-supervised Knowledge Distillation for Few-shot Learning. arXiv:2006.09785 [code]
Learning with Noisy Class Labels for Instance Segmentation. ECCV 2020
Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation. Wang, Liwei et al. arXiv:2007.01951

Knowledge from intermediate layers

Fitnets: Hints for thin deep nets. Romero, Adriana et al. arXiv:1412.6550
Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. Zagoruyko et al. ICLR 2017
Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks. Zhang, Zhi et al. arXiv:1710.09505
A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning. Yim, Junho et al. CVPR 2017
Paraphrasing complex network: Network compression via factor transfer. Kim, Jangho et al. NIPS 2018
Knowledge transfer with jacobian matching. ICML 2018
Self-supervised knowledge distillation using singular value decomposition. Lee, Seung Hyun et al. ECCV 2018
Learning Deep Representations with Probabilistic Knowledge Transfer. Passalis et al. ECCV 2018
Variational Information Distillation for Knowledge Transfer. Ahn, Sungsoo et al. CVPR 2019
Knowledge Distillation via Instance Relationship Graph. Liu, Yufan et al. CVPR 2019
Knowledge Distillation via Route Constrained Optimization. Jin, Xiao et al. ICCV 2019
Similarity-Preserving Knowledge Distillation. Tung, Frederick, and Mori Greg. ICCV 2019
MEAL: Multi-Model Ensemble via Adversarial Learning. Shen,Zhiqiang, He,Zhankui, and Xue Xiangyang. AAAI 2019
A Comprehensive Overhaul of Feature Distillation. Heo, Byeongho et al. ICCV 2019
Feature-map-level Online Adversarial Knowledge Distillation. ICML 2020
Distilling Object Detectors with Fine-grained Feature Imitation. ICLR 2020
Knowledge Squeezed Adversarial Network Compression. Changyong, Shu et al. AAAI 2020
Stagewise Knowledge Distillation. Kulkarni, Akshay et al. arXiv: 1911.06786
Knowledge Distillation from Internal Representations. AAAI 2020
Knowledge Flow:Improve Upon Your Teachers. ICLR 2019
LIT: Learned Intermediate Representation Training for Model Compression. ICML 2019
Improving the Adversarial Robustness of Transfer Learning via Noisy Feature Distillation. Chin, Ting-wu et al. arXiv:2002.02998
Knapsack Pruning with Inner Distillation. Aflalo, Yonathan et al. arXiv:2002.08258
Residual Knowledge Distillation. Gao, Mengya et al. arXiv:2002.09168
Knowledge distillation via adaptive instance normalization. Yang, Jing et al. arXiv:2003.04289
Bert-of-Theseus: Compressing bert by progressive module replacing. Xu, Canwen et al. arXiv:2002.02925 [code]
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks. arXiv:2005.00727
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks. Meet et al. arXiv:2005.08110
Feature-map-level Online Adversarial Knowledge Distillation. Chung, Inseop et al. ICML 2020
Channel Distillation: Channel-Wise Attention for Knowledge Distillation. Zhou, Zaida et al. arXiv:2006.01683 [code]
Matching Guided Distillation. ECCV 2020
Differentiable Feature Aggregation Search for Knowledge Distillation. ECCV 2020
Interactive Knowledge Distillation. Fu, Shipeng et al. arXiv:2007.01476

Graph-based

Graph-based Knowledge Distillation by Multi-head Attention Network. Lee, Seunghyun and Song, Byung. Cheol arXiv:1907.02226
Graph Representation Learning via Multi-task Knowledge Distillation. arXiv:1911.05700
Deep geometric knowledge distillation with graphs. arXiv:1911.03080
Better and faster: Knowledge transfer from multiple self-supervised learning tasks via graph distillation for video classification. IJCAI 2018
Distillating Knowledge from Graph Convolutional Networks. Yang, Yiding et al. CVPR 2020

Mutual Information

Correlation Congruence for Knowledge Distillation. Peng, Baoyun et al. ICCV 2019
Similarity-Preserving Knowledge Distillation. Tung, Frederick, and Mori Greg. ICCV 2019
Variational Information Distillation for Knowledge Transfer. Ahn, Sungsoo et al. CVPR 2019
Contrastive Representation Distillation. Tian, Yonglong et al. ICLR 2020 [RepDistill]
Online Knowledge Distillation via Collaborative Learning. Guo, Qiushan et al. CVPR 2020
Peer Collaborative Learning for Online Knowledge Distillation. Wu, Guile & Gong, Shaogang. arXiv:2006.04147
Online Knowledge Distillation via Collaborative Learning. Guo, Qiushan et al. CVPR 2020
Knowledge Transfer via Dense Cross-layer Mutual-distillation. ECCV 2020
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution. Yang, Taojiannan et al. ECCV 2020 [code]

Self-KD

Moonshine:Distilling with Cheap Convolutions. Crowley, Elliot J. et al. NIPS 2018
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation. Zhang, Linfeng et al. ICCV 2019
Learning Lightweight Lane Detection CNNs by Self Attention Distillation. Hou, Yuenan et al. ICCV 2019
BAM! Born-Again Multi-Task Networks for Natural Language Understanding. Clark, Kevin et al. ACL 2019,short
Self-Knowledge Distillation in Natural Language Processing. Hahn, Sangchul and Choi, Heeyoul. arXiv:1908.01851
Rethinking Data Augmentation: Self-Supervision and Self-Distillation. Lee, Hankook et al. ICLR 2020
MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks. arXiv:1911.09418
Self-Distillation Amplifies Regularization in Hilbert Space. Mobahi, Hossein et al. arXiv:2002.05715
MINILM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers. Wang, Wenhui et al. arXiv:2002.10957
Regularizing Class-wise Predictions via Self-knowledge Distillation. CVPR 2020 [code]
Self-Distillation as Instance-Specific Label Smoothing. Zhang, Zhilu & Sabuncu, Mert R. arXiv:2006.05065

Structured Knowledge

Paraphrasing Complex Network:Network Compression via Factor Transfer. Kim, Jangho et al. NIPS 2018
Relational Knowledge Distillation. Park, Wonpyo et al. CVPR 2019
Knowledge Distillation via Instance Relationship Graph. Liu, Yufan et al. CVPR 2019
Contrastive Representation Distillation. Tian, Yonglong et al. ICLR 2020
Teaching To Teach By Structured Dark Knowledge. ICLR 2020
Inter-Region Affinity Distillation for Road Marking Segmentation. Hou, Yuenan et al. CVPR 2020 [code]
Heterogeneous Knowledge Distillation using Information Flow Modeling. Passalis et al. CVPR 2020 [code]
Asymmetric metric learning for knowledge transfer. Budnik, Mateusz & Avrithis, Yannis. arXiv:2006.16331
Local Correlation Consistency for Knowledge Distillation. 2020
Few-Shot Class-Incremental Learning. Tao, Xiaoyu et al. CVPR 2020

Privileged Information

Learning using privileged information: similarity control and knowledge transfer. Vapnik, Vladimir and Rauf, Izmailov. MLR 2015
Unifying distillation and privileged information. Lopez-Paz, David et al. ICLR 2016
Model compression via distillation and quantization. Polino, Antonio et al. ICLR 2018
KDGAN:Knowledge Distillation with Generative Adversarial Networks. Wang, Xiaojie. NIPS 2018
Efficient Video Classification Using Fewer Frames. Bhardwaj, Shweta et al. CVPR 2019
Retaining privileged information for multi-task learning. Tang, Fengyi et al. KDD 2019
A Generalized Meta-loss function for regression and classification using privileged information. Asif, Amina et al. arXiv:1811.06885
Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks. Gao, Di & Zhuo, Cheng. AAAI 2020

KD + GAN

Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks. Xu, Zheng et al. arXiv:1709.00513
KTAN: Knowledge Transfer Adversarial Network. Liu, Peiye et al. arXiv:1810.08126
KDGAN:Knowledge Distillation with Generative Adversarial Networks. Wang, Xiaojie. NIPS 2018
Adversarial Learning of Portable Student Networks. Wang, Yunhe et al. AAAI 2018
Adversarial Network Compression. Belagiannis, Vasileios et al. ECCV 2018
Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks. ICASSP 2018
Adversarial Distillation for Efficient Recommendation with External Knowledge. TOIS 2018
Training student networks for acceleration with conditional adversarial networks. Xu, Zheng et al. BMVC 2018
DAFL:Data-Free Learning of Student Networks. Chen, Hanting et al. ICCV 2019
MEAL: Multi-Model Ensemble via Adversarial Learning. Shen,Zhiqiang, He,Zhankui, and Xue Xiangyang. AAAI 2019
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary. Heo, Byeongho et al. AAAI 2019
Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection. Liu, Jian et al. AAAI 2019
Adversarially Robust Distillation. Goldblum, Micah et al. AAAI 2020
GAN-Knowledge Distillation for one-stage Object Detection. Hong, Wei et al. arXiv:1906.08467
Lifelong GAN: Continual Learning for Conditional Image Generation. Kundu et al. arXiv:1908.03884
Compressing GANs using Knowledge Distillation. Aguinaldo, Angeline et al. arXiv:1902.00159
Feature-map-level Online Adversarial Knowledge Distillation. ICML 2020
MineGAN: effective knowledge transfer from GANs to target domains with few images. Wang, Yaxing et al. CVPR 2020
Distilling portable Generative Adversarial Networks for Image Translation. Chen, Hanting et al. AAAI 2020
GAN Compression: Efficient Architectures for Interactive Conditional GANs. Junyan Zhu et al. CVPR 2020 [code]
Adversarial network compression. Belagiannis et al. ECCV 2018

KD + Meta-learning

Few Sample Knowledge Distillation for Efficient Network Compression. Li, Tianhong et al. CVPR 2020
Learning What and Where to Transfer. Jang, Yunhun et al, ICML 2019
Transferring Knowledge across Learning Processes. Moreno, Pablo G et al. ICLR 2019
Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval. Liu, Qing et al. ICCV 2019
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. Dvornik, Nikita et al. ICCV 2019
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation. arXiv:1911.05329v1
Progressive Knowledge Distillation For Generative Modeling. ICLR 2020
Few Shot Network Compression via Cross Distillation. AAAI 2020
MetaDistiller: Network Self-boosting via Meta-learned Top-down Distillation. ECCV 2020

Data-free KD

Data-Free Knowledge Distillation for Deep Neural Networks. NIPS 2017
Zero-Shot Knowledge Distillation in Deep Networks. ICML 2019
DAFL:Data-Free Learning of Student Networks. ICCV 2019
Zero-shot Knowledge Transfer via Adversarial Belief Matching. Micaelli, Paul and Storkey, Amos. NIPS 2019
Dream Distillation: A Data-Independent Model Compression Framework. Kartikeya et al. ICML 2019
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion. Yin, Hongxu et al. CVPR 2020
Data-Free Adversarial Distillation. Fang, Gongfan et al. CVPR 2020
The Knowledge Within: Methods for Data-Free Model Compression. Haroush, Matan et al. CVPR 2020
Knowledge Extraction with No Observable Data. Yoo, Jaemin et al. NIPS 2019 [code]
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN. CVPR 2020
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier. Addepalli, Sravanti et al. arXiv:1912.11960
Generative Low-bitwidth Data Free Quantization. Xu, Shoukai et al. arXiv:2003.03603
This dataset does not exist: training models from generated images. arXiv:1911.02888
MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation. Sanjay et al. arXiv:2005.03161
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data. Such et al. ECCV 2020
Billion-scale semi-supervised learning for image classification. FAIR. arXiv:1905.00546 [code]

other data-free model compression:

Data-free Parameter Pruning for Deep Neural Networks. Srinivas, Suraj et al. arXiv:1507.06149
Data-Free Quantization Through Weight Equalization and Bias Correction. Nagel, Markus et al. ICCV 2019
DAC: Data-free Automatic Acceleration of Convolutional Networks. Li, Xin et al. WACV 2019

KD + AutoML

Improving Neural Architecture Search Image Classifiers via Ensemble Learning. Macko, Vladimir et al. arXiv:1903.06236
Blockwisely Supervised Neural Architecture Search with Knowledge Distillation. Li, Changlin et al. arXiv:1911.13053v1
Towards Oracle Knowledge Distillation with Neural Architecture Search. Kang, Minsoo et al. AAAI 2020
Search for Better Students to Learn Distilled Knowledge. Gu, Jindong & Tresp, Volker arXiv:2001.11612
Circumventing Outliers of AutoAugment with Knowledge Distillation. Wei, Longhui et al. arXiv:2003.11342
Network Pruning via Transformable Architecture Search. Dong, Xuanyi & Yang, Yi. NIPS 2019
Search to Distill: Pearls are Everywhere but not the Eyes. Liu Yu et al. CVPR 2020
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks. Fu, Yonggan et al. ICML 2020 [code]

KD + RL

N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning. Ashok, Anubhav et al. ICLR 2018
Knowledge Flow:Improve Upon Your Teachers. Liu, Iou-jen et al. ICLR 2019
Transferring Knowledge across Learning Processes. Moreno, Pablo G et al. ICLR 2019
Exploration by random network distillation. Burda, Yuri et al. ICLR 2019
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning. Hong, Zhang-Wei et al. arXiv:2002.00149
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach. Xue, Zeyue et al. arXiv:2002.02202
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning. Cha, han et al. arXiv:2005.06105
Dual Policy Distillation. Lai, Kwei-Herng et al. arXiv:2006.04061

Multi-teacher KD

Learning from Multiple Teacher Networks. You, Shan et al. KDD 2017
Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR 2017
Knowledge Adaptation: Teaching to Adapt. Arxiv:1702.02052
Deep Model Compression: Distilling Knowledge from Noisy Teachers. Sau, Bharat Bhusan et al. arXiv:1610.09650v2
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Tarvainen, Antti and Valpola, Harri. NIPS 2017
Born-Again Neural Networks. Furlanello, Tommaso et al. ICML 2018
Deep Mutual Learning. Zhang, Ying et al. CVPR 2018
Knowledge distillation by on-the-fly native ensemble. Lan, Xu et al. NIPS 2018
Collaborative learning for deep neural networks. Song, Guocong and Chai, Wei. NIPS 2018
Data Distillation: Towards Omni-Supervised Learning. Radosavovic, Ilija et al. CVPR 2018
Multilingual Neural Machine Translation with Knowledge Distillation. ICLR 2019
Unifying Heterogeneous Classifiers with Distillation. Vongkulbhisal et al. CVPR 2019
Distilled Person Re-Identification: Towards a More Scalable System. Wu, Ancong et al. CVPR 2019
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. Dvornik, Nikita et al. ICCV 2019
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System. Yang, Ze et al. WSDM 2020
FEED: Feature-level Ensemble for Knowledge Distillation. Park, SeongUk and Kwak, Nojun. AAAI 2020
Stochasticity and Skip Connection Improve Knowledge Transfer. Lee, Kwangjin et al. ICLR 2020
Online Knowledge Distillation with Diverse Peers. Chen, Defang et al. AAAI 2020
Hydra: Preserving Ensemble Diversity for Model Distillation. Tran, Linh et al. arXiv:2001.04694
Distilled Hierarchical Neural Ensembles with Adaptive Inference Cost. Ruiz, Adria et al. arXv:2003.01474
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition. Gao, Yan et al. arXiv:2005.09310
Adaptive multi-teacher multi-level knowledge distillation. Yuang Liu et al. Neurocomputing, 2020
Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery. ECCV 2020
Collaborative Learning for Faster StyleGAN Embedding. Guan, Shanyan et al. arXiv:2007.01758
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection. Chen, Cong et al. IEEE 2020 [code]
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation. MICCAI 2020

Knowledge Amalgamation（KA) - zju-VIPA

VIPA - KA

Amalgamating Knowledge towards Comprehensive Classification. Shen, Chengchao et al. AAAI 2019
Amalgamating Filtered Knowledge : Learning Task-customized Student from Multi-task Teachers. Ye, Jingwen et al. IJCAI 2019
Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning. Luo, Sihui et al. IJCAI 2019
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More. Ye, Jingwen et al. CVPR 2019
Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation. ICCV 2019
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN. CVPR 2020

Cross-modal KD & DA

SoundNet: Learning Sound Representations from Unlabeled Video SoundNet Architecture. Aytar, Yusuf et al. ECCV 2016
Cross Modal Distillation for Supervision Transfer. Gupta, Saurabh et al. CVPR 2016
Emotion recognition in speech using cross-modal transfer in the wild. Albanie, Samuel et al. ACM MM 2018
Through-Wall Human Pose Estimation Using Radio Signals. Zhao, Mingmin et al. CVPR 2018
Compact Trilinear Interaction for Visual Question Answering. Do, Tuong et al. ICCV 2019
Cross-Modal Knowledge Distillation for Action Recognition. Thoker, Fida Mohammad and Gall, Juerge. ICIP 2019
Learning to Map Nearly Anything. Salem, Tawfiq et al. arXiv:1909.06928
Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval. Liu, Qing et al. ICCV 2019
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation. Kundu et al. ICCV 2019
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency. Chen, Yun-Chun et al. CVPR 2019
XD:Cross lingual Knowledge Distillation for Polyglot Sentence Embeddings. ICLR 2020
Effective Domain Knowledge Transfer with Soft Fine-tuning. Zhao, Zhichen et al. arXiv:1909.02236
ASR is all you need: cross-modal distillation for lip reading. Afouras et al. arXiv:1911.12747v1
Knowledge distillation for semi-supervised domain adaptation. arXiv:1908.07355
Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition. Meng, Zhong et al. arXiv:2001.01798
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation. ICCV 2019
Attention Bridging Network for Knowledge Transfer. Li, Kunpeng et al. ICCV 2019
Unpaired Multi-modal Segmentation via Knowledge Distillation. Dou, Qi et al. arXiv:2001.03111
Multi-source Distilling Domain Adaptation. Zhao, Sicheng et al. arXiv:1911.11554
Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing. Hu, Hengtong et al. CVPR 2020
Improving Semantic Segmentation via Self-Training. Zhu, Yi et al. arXiv:2004.14960
Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation. arXiv:2005.08213
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation. arXiv:2005.07839
Knowledge as Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge. Zhao, Long et al. CVPR 2020
Large-Scale Domain Adaptation via Teacher-Student Learning. Li, Jinyu et al. arXiv:1708.05466
Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data. Fayek, Haytham M. & Kumar, Anurag. IJCAI 2020
Distilling Cross-Task Knowledge via Relationship Matching. Ye, Han-Jia. et al. CVPR 2020 [code]
Modality distillation with multiple stream networks for action recognition. Garcia, Nuno C. et al. ECCV 2018
Domain Adaptation through Task Distillation. ECCV 2020

Application of KD

Face model compression by distilling knowledge from neurons. Luo, Ping et al. AAAI 2016
Learning efficient object detection models with knowledge distillation. Chen, Guobin et al. NIPS 2017
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy. Mishra, Asit et al. NIPS 2018
Distilled Person Re-identification: Towars a More Scalable System. Wu, Ancong et al. CVPR 2019
Efficient Video Classification Using Fewer Frames. Bhardwaj, Shweta et al. CVPR 2019
Fast Human Pose Estimation. Zhang, Feng et al. CVPR 2019
Distilling knowledge from a deep pose regressor network. Saputra et al. arXiv:1908.00858 (2019)
Learning Lightweight Lane Detection CNNs by Self Attention Distillation. Hou, Yuenan et al. ICCV 2019
Structured Knowledge Distillation for Semantic Segmentation. Liu, Yifan et al. CVPR 2019
Relation Distillation Networks for Video Object Detection. Deng, Jiajun et al. ICCV 2019
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection. Dong, Xuanyi and Yang, Yi. ICCV 2019
Progressive Teacher-student Learning for Early Action Prediction. Wang, Xionghui et al. CVPR 2019
Lightweight Image Super-Resolution with Information Multi-distillation Network. Hui, Zheng et al. ICCVW 2019
AWSD:Adaptive Weighted Spatiotemporal Distillation for Video Representation. Tavakolian, Mohammad et al. ICCV 2019
Dynamic Kernel Distillation for Efficient Pose Estimation in Videos. Nie, Xuecheng et al. ICCV 2019
Teacher Guided Architecture Search. Bashivan, Pouya and Tensen, Mark. ICCV 2019
Online Model Distillation for Efficient Video Inference. Mullapudi et al. ICCV 2019
Distilling Object Detectors with Fine-grained Feature Imitation. Wang, Tao et al. CVPR 2019
Relation Distillation Networks for Video Object Detection. Deng, Jiajun et al. ICCV 2019
Knowledge Distillation for Incremental Learning in Semantic Segmentation. arXiv:1911.03462
MOD: A Deep Mixture Model with Online Knowledge Distillation for Large Scale Video Temporal Concept Localization. arXiv:1910.12295
Teacher-Students Knowledge Distillation for Siamese Trackers. arXiv:1907.10586
LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning. Zhao, Albert et al. CVPR 2020(pre)
Knowledge Distillation for Brain Tumor Segmentation. arXiv:2002.03688
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes. Chen, Yuhua et al. CVPR 2018
Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices. WWW 2020
Multi-Representation Knowledge Distillation For Audio Classification. Gao, Liang et al. arXiv:2002.09607
Collaborative Distillation for Ultra-Resolution Universal Style Transfer. Wang, Huan et al. CVPR 2020 [code]
ShadowTutor: Distributed Partial Distillation for Mobile Video DNN Inference. Chung, Jae-Won et al. ICPP 2020 [code]
Object Relational Graph with Teacher-Recommended Learning for Video Captioning. Zhang, Ziqi et al. CVPR 2020
Spatio-Temporal Graph for Video Captioning with Knowledge distillation. CVPR 2020 [code]
Squeezed Deep 6DoF Object Detection Using Knowledge Distillation. Felix, Heitor et al. arXiv:2003.13586
Distilled Semantics for Comprehensive Scene Understanding from Videos. Tosi, Fabio et al. arXiv:2003.14030
Parallel WaveNet: Fast high-fidelity speech synthesis. Van et al. ICML 2018
Distill Knowledge From NRSfM for Weakly Supervised 3D Pose Learning. Wang Chaoyang et al. ICCV 2019
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow. Murugesan et al. MIDL 2020
Geometry-Aware Distillation for Indoor Semantic Segmentation. Jiao, Jianbo et al. CVPR 2019
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection. ICCV 2019
Distill Image Dehazing with Heterogeneous Task Imitation. Hong, Ming et al. CVPR 2020
Knowledge Distillation for Action Anticipation via Label Smoothing. Camporese et al. arXiv:2004.07711
More Grounded Image Captioning by Distilling Image-Text Matching Model. Zhou, Yuanen et al. CVPR 2020
Distilling Knowledge from Refinement in Multiple Instance Detection Networks. Zeni, Luis Felipe & Jung, Claudio. arXiv:2004.10943
A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data. SIGIR 2020
Enabling Incremental Knowledge Transfer for Object Detection at the Edge. arXiv:2004.05746
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings. Bergmann, Paul et al. CVPR 2020
TA-Student VQA: Multi-Agents Training by Self-Questioning. Xiong, Peixi & Wu Ying. CVPR 2020
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. Jiang, Lu et al. ICML 2018
A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection. Chen, Zhihao et al. CVPR 2020 [code]
Learning Lightweight Face Detector with Knowledge Distillation. Zhang Shifeng et al. IEEE 2019
Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillation. ICIP 2019
Distilling Object Detectors with Task Adaptive Regularization. Sun, Ruoyu et al. arXiv:2006.13108
Privileged Features Distillation at Taobao Recommendations. Xu, Chen et al. KDD 2020
Intra-class Compactness Distillation for Semantic Segmentation. ECCV 2020
Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer. ECCV 2020
DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild. ECCV 2020
Self-similarity Student for Partial Label Histopathology Image Segmentation. ECCV 2020
Self-Supervised GAN Compression. Yu, Chong & Pool, Jeff. arXiv:2007.01491
Robust Re-Identification by Multiple Views Knowledge Distillation. Porrello et al. ECCV 2020 [code]
LabelEnc: A New Intermediate Supervision Method for Object Detection. Hao, Miao et al. arXiv:2007.03282
Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer. Chen, Xinghao et al. ECCV 2020
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition. Si, Chenyang et al. ECCV 2020

for NLP

Patient Knowledge Distillation for BERT Model Compression. Sun, Siqi et al. arXiv:1908.09355
TinyBERT: Distilling BERT for Natural Language Understanding. Jiao, Xiaoqi et al. arXiv:1909.10351
Learning to Specialize with Knowledge Distillation for Visual Question Answering. NIPS 2018
Knowledge Distillation for Bilingual Dictionary Induction. EMNLP 2017
A Teacher-Student Framework for Maintainable Dialog Manager. EMNLP 2018
Understanding Knowledge Distillation in Non-Autoregressive Machine Translation. arxiv 2019
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. Sanh, Victor et al. arXiv:1910.01108
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. Turc, Iulia et al. arXiv:1908.08962
On Knowledge distillation from complex networks for response prediction. Arora, Siddhartha et al. NAACL 2019
Distilling the Knowledge of BERT for Text Generation. arXiv:1911.03829v1
Understanding Knowledge Distillation in Non-autoregressive Machine Translation. arXiv:1911.02727
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices. Sun, Zhiqing et al. ACL 2020
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation. Weng, Rongxiang et al. AAAI 2020
TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval. Lu, Wenhao et al. KDD 2020
Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation. Xu, Yige et al. arXiv:2002.10345
FastBERT: a Self-distilling BERT with Adaptive Inference Time. Liu, Weijie et al. ACL 2020
LightRec: a Memory and Search-Efficient Recommender System. Lian Defu et al. WWW 2020
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression. Mao, Yihuan et al. arXiv:2004.04124
DynaBERT: Dynamic BERT with Adaptive Width and Depth. Hou, Lu et al. arXiv:2004.04037
Structure-Level Knowledge Distillation For Multilingual Sequence Labeling. Wang, Xinyu et al. ACL 2020
Distilled embedding: non-linear embedding factorization using knowledge distillation. Lioutas, Vasileios et al. arXiv:1910.06720
TinyMBERT: Multi-Stage Distillation Framework for Massive Multi-lingual NER. Mukherjee & Awadallah. ACL 2020
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation. Sun, Haipeng et al. arXiv:2004.10171
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Reimers, Nils & Gurevych, Iryna arXiv:2004.09813
Distilling Knowledge for Fast Retrieval-based Chat-bots. Tahami et al. arXiv:2004.11045
Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language. ACL 2020
Local Clustering with Mean Teacher for Semi-supervised Learning. arXiv:2004.09665
Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher. arXiv:2004.08780
Syntactic Structure Distillation Pretraining For Bidirectional Encoders. arXiv: 2005.13482
Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation. arXiv:2003.02877
Distilling Neural Networks for Faster and Greener Dependency Parsing. arXiv:2006.00844
Distilling Knowledge from Well-informed Soft Labels for Neural Relation Extraction. AAAI 2020 [code]
More Grounded Image Captioning by Distilling Image-Text Matching Model. Zhou, Yuanen et al. CVPR 2020
Multimodal Learning with Incomplete Modalities by Knowledge Distillation. Wang, Qi et al. KDD 2020

Model Pruning or Quantization

Accelerating Convolutional Neural Networks with Dominant Convolutional Kernel and Knowledge Pre-regression. ECCV 2016
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning. Ashok, Anubhav et al. ICLR 2018
Slimmable Neural Networks. Yu, Jiahui et al. ICLR 2018
Co-Evolutionary Compression for Unpaired Image Translation. Shu, Han et al. ICCV 2019
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning. Liu, Zechun et al. ICCV 2019
LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning. ICLR 2020
Pruning with hints: an efficient framework for model acceleration. ICLR 2020
Training convolutional neural networks with cheap convolutions and online distillation. arXiv:1909.13063
Cooperative Pruning in Cross-Domain Deep Neural Network Compression. Chen, Shangyu et al. IJCAI 2019
QKD: Quantization-aware Knowledge Distillation. Kim, Jangho et al. arXiv:1911.12491v1
Neural Network Pruning with Residual-Connections and Limited-Data. Luo, Jian-Hao & Wu, Jianxin. CVPR 2020
Training Quantized Neural Networks with a Full-precision Auxiliary Module. Zhuang, Bohan et al. CVPR 2020
Towards Effective Low-bitwidth Convolutional Neural Networks. Zhuang, Bohan et al. CVPR 2018
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations. Zhuang, Bohan et al. arXiv:1908.04680
Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation. Le et al. arXiv:2006.11487 [code]
Knowledge Distillation Beyond Model Compression. Choi, Arthur et al. arxiv:2007.01493
Distillation Guided Residual Learning for Binary Convolutional Neural Networks. Ye, Jianming et al. ECCV 20

Beyond

Do deep nets really need to be deep?. Ba,Jimmy, and Rich Caruana. NIPS 2014
When Does Label Smoothing Help? Müller, Rafael, Kornblith, and Hinton. NIPS 2019
Towards Understanding Knowledge Distillation. Phuong, Mary and Lampert, Christoph. AAAI 2019
Harnessing deep neural networks with logical rules. ACL 2016
Adaptive Regularization of Labels. Ding, Qianggang et al. arXiv:1908.05474
Knowledge Isomorphism between Neural Networks. Liang, Ruofan et al. arXiv:1908.01581
Neural Network Distiller: A Python Package For DNN Compression Research. arXiv:1910.12232
(survey)Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation. arXiv:1912.13179
Understanding and Improving Knowledge Distillation. Tang, Jiaxi et al. arXiv:2002.03532
The State of Knowledge Distillation for Classification. Ruffy, Fabian and Chahal, Karanbir. arXiv:1912.10850 [code]
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing. HIT and iFLYTEK. arXiv:2002.12620
Explaining Knowledge Distillation by Quantifying the Knowledge. Zhang, Quanshi et al. CVPR 2020
DeepVID: deep visual interpretation and diagnosis for image classifiers via knowledge distillation. IEEE Trans, 2019.
On the Unreasonable Effectiveness of Knowledge Distillation: Analysis in the Kernel Regime. Rahbar, Arman et al. arXiv:2003.13438
(survey)Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks. Wang, Lin & Yoon, Kuk-Jin. arXiv:2004.05937
Why distillation helps: a statistical perspective. arXiv:2005.10419
Transferring Inductive Biases through Knowledge Distillation. Abnar, Samira et al. arXiv:2006.00555
Does label smoothing mitigate label noise? Lukasik, Michal et al. ICML 2020
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation. Das, Deepan et al. arXiv:2006.03810
Knowledge Distillation: A Survey. Gou, Jianping et al. arXiv:2006.05525
Does Adversarial Transferability Indicate Knowledge Transferability? Liang, Kaizhao et al. arXiv:2006.14512
On the Demystification of Knowledge Distillation: A Residual Network Perspective. Jha et al. arXiv:2006.16589

Note: All papers pdf can be found and downloaded on Bing or Google.

Source: https://github.com/FLHonker/Awesome-Knowledge-Distillation

Contact:Yuang Liu(frankliu624@outlook.com), ECNU.

IsaccGuang/Awesome-Knowledge-Distillation

Awesome Knowledge-Distillation

Different forms of knowledge

Knowledge from logits

Knowledge from intermediate layers

Graph-based

Mutual Information

Self-KD

Structured Knowledge

Privileged Information

KD + GAN

KD + Meta-learning

Data-free KD

KD + AutoML

KD + RL

Multi-teacher KD

Knowledge Amalgamation（KA) - zju-VIPA

Cross-modal KD & DA

Application of KD

for NLP

Model Pruning or Quantization

Beyond