Awesome Knowledge-Distillation

2019/11/26 19:03
阅读数 51

 

Awesome Knowledge-Distillation

2019-11-26 19:02:16

 

Sourcehttps://github.com/FLHonker/Awesome-Knowledge-Distillation 

 

Different forms of knowledge

Knowledge from logits

  1. Distilling the knowledge in a neural network. Hinton et al. arXiv:1503.02531
  2. Learning from Noisy Labels with Distillation. Li, Yuncheng et al. ICCV 2017
  3. Knowledge distillation by on-the-fly native ensemble. Lan, Xu et al. NIPS 2018
  4. Learning Metrics from Teachers: Compact Networks for Image Embedding. Yu, Lu et al. CVPR 2019
  5. Relational Knowledge Distillation. Park, Wonpyo et al, CVPR 2019
  6. Like What You Like: Knowledge Distill via Neuron Selectivity Transfer. Huang, Zehao and Wang, Naiyan. 2017
  7. On Knowledge Distillation from Complex Networks for Response Prediction. Arora, Siddhartha et al. NAACL 2019
  8. On the Efficacy of Knowledge Distillation. Cho, Jang Hyun and Hariharan, Bharath. arXiv:1910.01348
  9. [noval]Revisit Knowledge Distillation: a Teacher-free Framework. Yuan, Li et al. arXiv:1909.11723
  10. Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher. Mirzadeh et al. arXiv:1902.03393
  11. Ensemble Distribution Distillation. ICLR 2020
  12. Noisy Collaboration in Knowledge Distillation. ICLR 2020
  13. On Compressing U-net Using Knowledge Distillation. arXiv:1812.00249
  14. Distillation-Based Training for Multi-Exit Architectures. Phuong, Mary and Lampert, Christoph H. ICCV 2019
  15. Self-training with Noisy Student improves ImageNet classification. Xie, Qizhe et al.(Google) arXiv:1911.04252
  16. Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework. arXiv:1910.12061
  17. Preparing Lessons: Improve Knowledge Distillation with Better Supervision. arXiv:1911.07471
  18. Adaptive Regularization of Labels. arXiv:1908.05474
  19. Positive-Unlabeled Compression on the Cloud. Xu, Yixing(HUAWEI) et al. NIPS 2019

Knowledge from intermediate layers

  1. Fitnets: Hints for thin deep nets. Romero, Adriana et al. arXiv:1412.6550
  2. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. Zagoruyko et al. ICLR 2017
  3. Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks. Zhang, Zhi et al. arXiv:1710.09505
  4. A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning. Yim, Junho et al. CVPR 2017
  5. Paraphrasing complex network: Network compression via factor transfer. Kim, Jangho et al. NIPS 2018
  6. Knowledge transfer with jacobian matching. ICML 2018
  7. Self-supervised knowledge distillation using singular value decomposition. Lee, Seung Hyun et al. ECCV 2018
  8. Variational Information Distillation for Knowledge Transfer. Ahn, Sungsoo et al. CVPR 2019 9
  9. Knowledge Distillation via Instance Relationship Graph. Liu, Yufan et al. CVPR 2019
  10. Knowledge Distillation via Route Constrained Optimization. Jin, Xiao et al. ICCV 2019
  11. Similarity-Preserving Knowledge Distillation. Tung, Frederick, and Mori Greg. ICCV 2019
  12. MEAL: Multi-Model Ensemble via Adversarial Learning. Shen,Zhiqiang, He,Zhankui, and Xue Xiangyang. AAAI 2019
  13. A Comprehensive Overhaul of Feature Distillation. Heo, Byeongho et al. ICCV 2019
  14. Feature-map-level Online Adversarial Knowledge Distillation. ICLR 2020
  15. Distilling Object Detectors with Fine-grained Feature Imitation. ICLR 2020
  16. Knowledge Squeezed Adversarial Network Compression. Changyong, Shu et al. AAAI 2020
  17. Stagewise Knowledge Distillation. Kulkarni, Akshay et al. arXiv: 1911.06786
  18. Knowledge Distillation from Internal Representations. arXiv:1910.03723
  19. Knowledge Flow:Improve Upon Your Teachers. ICLR 2019

Graph-based

  1. Graph-based Knowledge Distillation by Multi-head Attention Network. Lee, Seunghyun and Song, Byung. Cheol arXiv:1907.02226
  2. Graph Representation Learning via Multi-task Knowledge Distillation. arXiv:1911.05700
  3. Deep geometric knowledge distillation with graphs. arXiv:1911.03080

Mutual Information

  1. Correlation Congruence for Knowledge Distillation. Peng, Baoyun et al. ICCV 2019
  2. Similarity-Preserving Knowledge Distillation. Tung, Frederick, and Mori Greg. ICCV 2019
  3. Variational Information Distillation for Knowledge Transfer. Ahn, Sungsoo et al. CVPR 2019
  4. Contrastive Representation Distillation. Tian, Yonglong et al. arXiv: 1910.10699

Self-KD

  1. Moonshine:Distilling with Cheap Convolutions. Crowley, Elliot J. et al. NIPS 2018
  2. Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation. Zhang, Linfeng et al. ICCV 2019
  3. Learning Lightweight Lane Detection CNNs by Self Attention Distillation. Hou, Yuenan et al. ICCV 2019
  4. BAM! Born-Again Multi-Task Networks for Natural Language Understanding. Clark, Kevin et al. ACL 2019,short
  5. Self-Knowledge Distillation in Natural Language Processing. Hahn, Sangchul and Choi, Heeyoul. arXiv:1908.01851
  6. Rethinking Data Augmentation: Self-Supervision and Self-Distillation. Lee, Hankook et al. ICLR 2020
  7. Regularizing Predictions via Class wise Self knowledge Distillation. ICLR 2020
  8. MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks. arXiv:1911.09418

Structured Knowledge

  1. Paraphrasing Complex Network:Network Compression via Factor Transfer. Kim, Jangho et al. NIPS 2018
  2. Relational Knowledge Distillation. Park, Wonpyo et al. CVPR 2019
  3. Knowledge Distillation via Instance Relationship Graph. Liu, Yufan et al. CVPR 2019
  4. Contrastive Representation Distillation. Tian, Yonglong et al. arXiv: 1910.10699
  5. Teaching To Teach By Structured Dark Knowledge. ICLR 2020

Privileged Information

  1. Learning using privileged information: similarity control and knowledge transfer. Vapnik, Vladimir and Rauf, Izmailov. MLR 2015
  2. Unifying distillation and privileged information. Lopez-Paz, David et al. ICLR 2016
  3. Model compression via distillation and quantization. Polino, Antonio et al. ICLR 2018
  4. KDGAN:Knowledge Distillation with Generative Adversarial Networks. Wang, Xiaojie. NIPS 2018
  5. [noval]Efficient Video Classification Using Fewer Frames. Bhardwaj, Shweta et al. CVPR 2019
  6. Retaining privileged information for multi-task learning. Tang, Fengyi et al. KDD 2019
  7. A Generalized Meta-loss function for regression and classification using privileged information. Asif, Amina et al. arXiv:1811.06885

KD + GAN

  1. Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks. Xu, Zheng et al. arXiv:1709.00513
  2. KTAN: Knowledge Transfer Adversarial Network. Liu, Peiye et al. arXiv:1810.08126
  3. KDGAN:Knowledge Distillation with Generative Adversarial Networks. Wang, Xiaojie. NIPS 2018
  4. Adversarial Learning of Portable Student Networks. Wang, Yunhe et al. AAAI 2018
  5. Adversarial Network Compression. Belagiannis, Vasileios et al. ECCV 2018
  6. Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks. ICASSP 2018
  7. Adversarial Distillation for Efficient Recommendation with External Knowledge. TOIS 2018
  8. Training student networks for acceleration with conditional adversarial networks. Xu, Zheng et al. BMVC 2018
  9. [noval]DAFL:Data-Free Learning of Student Networks. Chen, Hanting et al. ICCV 2019
  10. MEAL: Multi-Model Ensemble via Adversarial Learning. Shen,Zhiqiang, He,Zhankui, and Xue Xiangyang. AAAI 2019
  11. Knowledge Distillation with Adversarial Samples Supporting Decision Boundary. Heo, Byeongho et al. AAAI 2019
  12. Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection. Liu, Jian et al. AAAI 2019
  13. Adversarially Robust Distillation. Goldblum, Micah et al. arXiv:1905.09747
  14. GAN-Knowledge Distillation for one-stage Object Detection. Hong, Wei et al. arXiv:1906.08467
  15. Lifelong GAN: Continual Learning for Conditional Image Generation. Kundu et al. arXiv:1908.03884
  16. Compressing GANs using Knowledge Distillation. Aguinaldo, Angeline et al. arXiv:1902.00159
  17. Feature-map-level Online Adversarial Knowledge Distillation. ICLR 2020

KD + Meta-learning

  1. Few Sample Knowledge Distillation for Efficient Network Compression. Li, Tianhong et al. arxiv:1812.01839
  2. Learning What and Where to Transfer. Jang, Yunhun et al, ICML 2019
  3. Transferring Knowledge across Learning Processes. Moreno, Pablo G et al. ICLR 2019
  4. Few-Shot Image Recognition with Knowledge Transfer. Peng, Zhimao et al. ICCV 2019
  5. Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval. Liu, Qing et al. ICCV 2019
  6. Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation. arXiv:1911.05329v1
  7. Progressive Knowledge Distillation For Generative Modeling. ICLR 2020
  8. Few Shot Network Compression via Cross Distillation. AAAI 2020

Data-free KD

  1. Data-Free Knowledge Distillation for Deep Neural Networks. NIPS 2017
  2. Zero-Shot Knowledge Distillation in Deep Networks. ICML 2019
  3. DAFL:Data-Free Learning of Student Networks. ICCV 2019
  4. Zero-shot Knowledge Transfer via Adversarial Belief Matching. Micaelli, Paul and Storkey, Amos. NIPS 2019
  • other data-free model compression:
  1. Data-free Parameter Pruning for Deep Neural Networks. Srinivas, Suraj et al. arXiv:1507.06149
  2. Data-Free Quantization Through Weight Equalization and Bias Correction. Nagel, Markus et al. ICCV 2019

KD + AutoML

  1. Improving Neural Architecture Search Image Classifiers via Ensemble Learning. Macko, Vladimir et al. 2019

KD + RL

  1. N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning. Ashok, Anubhav et al. ICLR 2018
  2. Knowledge Flow:Improve Upon Your Teachers. Liu, Iou-jen et al. ICLR 2019
  3. Transferring Knowledge across Learning Processes. Moreno, Pablo G et al. ICLR 2019

Multi-teacher KD

  1. Learning from Multiple Teacher Networks. You, Shan et al. KDD 2017
  2. Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR 2017
  3. Knowledge Adaptation: Teaching to Adapt. Arxiv:1702.02052
  4. Deep Model Compression: Distilling Knowledge from Noisy Teachers. Sau, Bharat Bhusan et al. arXiv:1610.09650v2
  5. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Tarvainen, Antti and Valpola, Harri. NIPS 2017
  6. Born-Again Neural Networks. Furlanello, Tommaso et al. ICML 2018
  7. Deep Mutual Learning. Zhang, Ying et al. CVPR 2018
  8. Knowledge distillation by on-the-fly native ensemble. Lan, Xu et al. NIPS 2018
  9. Multilingual Neural Machine Translation with Knowledge Distillation. ICLR 2019
  10. Unifying Heterogeneous Classifiers with Distillation. Vongkulbhisal et al. CVPR 2019
  11. Distilled Person Re-Identification: Towards a More Scalable System. Wu, Ancong et al. CVPR 2019
  12. Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System. Yang, Ze et al. WSDM 2020
  13. FEED: Feature-level Ensemble for Knowledge Distillation. Park, SeongUk and Kwak, Nojun. arXiv:1909.10754(AAAI20 pre)
  14. Stochasticity and Skip Connection Improve Knowledge Transfer. Lee, Kwangjin et al. ICLR 2020

Cross-modal KD

  1. Cross Modal Distillation for Supervision Transfer. Gupta, Saurabh et al. CVPR 2016
  2. Emotion recognition in speech using cross-modal transfer in the wild. Albanie, Samuel et al. ACM MM 2018
  3. Through-Wall Human Pose Estimation Using Radio Signals. Zhao, Mingmin et al. CVPR 2018
  4. Compact Trilinear Interaction for Visual Question Answering. Do, Tuong et al. ICCV 2019
  5. Cross-Modal Knowledge Distillation for Action Recognition. Thoker, Fida Mohammad and Gall, Juerge. ICIP 2019
  6. Learning to Map Nearly Anything. Salem, Tawfiq et al. arXiv:1909.06928
  7. Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval. Liu, Qing et al. ICCV 2019
  8. UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation. Kundu et al. ICCV 2019
  9. CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency. Chen, Yun-Chun et al. CVPR 2019
  10. XD:Cross lingual Knowledge Distillation for Polyglot Sentence Embeddings. ICLR 2020
  11. Effective Domain Knowledge Transfer with Soft Fine-tuning. Zhao, Zhichen et al. arXiv:1909.02236

Application of KD

  1. Face model compression by distilling knowledge from neurons. Luo, Ping et al. AAAI 2016
  2. Learning efficient object detection models with knowledge distillation. Chen, Guobin et al. NIPS 2017
  3. Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy. Mishra, Asit et al. NIPS 2018
  4. Distilled Person Re-identification: Towars a More Scalable System. Wu, Ancong et al. CVPR 2019
  5. [noval]Efficient Video Classification Using Fewer Frames. Bhardwaj, Shweta et al. CVPR 2019
  6. Fast Human Pose Estimation. Zhang, Feng et al. CVPR 2019
  7. Distilling knowledge from a deep pose regressor network. Saputra et al. arXiv:1908.00858 (2019)
  8. Learning Lightweight Lane Detection CNNs by Self Attention Distillation. Hou, Yuenan et al. ICCV 2019
  9. Structured Knowledge Distillation for Semantic Segmentation. Liu, Yifan et al. CVPR 2019
  10. Relation Distillation Networks for Video Object Detection. Deng, Jiajun et al. ICCV 2019
  11. Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection. Dong, Xuanyi and Yang, Yi. ICCV 2019
  12. Progressive Teacher-student Learning for Early Action Prediction. Wang, Xionghui et al. CVPR2019
  13. Lightweight Image Super-Resolution with Information Multi-distillation Network. Hui, Zheng et al. ICCVW 2019
  14. AWSD:Adaptive Weighted Spatiotemporal Distillation for Video Representation. Tavakolian, Mohammad et al. ICCV 2019
  15. Dynamic Kernel Distillation for Efficient Pose Estimation in Videos. Nie, Xuecheng et al. ICCV 2019
  16. Teacher Guided Architecture Search. Bashivan, Pouya and Tensen, Mark. ICCV 2019
  17. Online Model Distillation for Efficient Video Inference. Mullapudi et al. ICCV 2019
  18. Distilling Object Detectors with Fine-grained Feature Imitation. Wang, Tao et al. CVPR2019
  19. Relation Distillation Networks for Video Object Detection. Deng, Jiajun et al. ICCV 2019
  20. Knowledge Distillation for Incremental Learning in Semantic Segmentation. arXiv:1911.03462
  21. MOD: A Deep Mixture Model with Online Knowledge Distillation for Large Scale Video Temporal Concept Localization. arXiv:1910.12295
  22. Teacher-Students Knowledge Distillation for Siamese Trackers. arXiv:1907.10586

for NLP

  1. Patient Knowledge Distillation for BERT Model Compression. Sun, Siqi et al. arXiv:1908.09355
  2. TinyBERT: Distilling BERT for Natural Language Understanding. Jiao, Xiaoqi et al. arXiv:1909.10351
  3. Learning to Specialize with Knowledge Distillation for Visual Question Answering. NIPS 2018
  4. Knowledge Distillation for Bilingual Dictionary Induction. EMNLP 2017
  5. A Teacher-Student Framework for Maintainable Dialog Manager. EMNLP 2018
  6. Understanding Knowledge Distillation in Non-Autoregressive Machine Translation. arxiv 2019
  7. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. Sanh, Victor et al. arXiv:1910.01108
  8. Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. Turc, Iulia et al. arXiv:1908.08962
  9. On Knowledge distillation from complex networks for response prediction. Arora, Siddhartha et al. NAACL 2019
  10. Distilling the Knowledge of BERT for Text Generation. arXiv:1911.03829v1
  11. Understanding Knowledge Distillation in Non-autoregressive Machine Translation. arXiv:1911.02727
  12. MobileBERT: Task-Agnostic Compression of BERT by Progressive Knowledge Transfer. ICLR 2020

Model Pruning

  1. N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning. Ashok, Anubhav et al. ICLR 2018
  2. Slimmable Neural Networks. Yu, Jiahui et al. ICLR 2018
  3. Co-Evolutionary Compression for Unpaired Image Translation. Shu, Han et al. ICCV 2019
  4. MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning. Liu, Zechun et al. ICCV 2019
  5. LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning. ICLR 2020
  6. Pruning with hints: an efficient framework for model acceleration. ICLR 2020
  7. Training convolutional neural networks with cheap convolutions and online distillation. arXiv:1909.13063
  8. Cooperative Pruning in Cross-Domain Deep Neural Network Compression. Chen, Shangyu et al. IJCAI 2019

Beyond

  1. Do deep nets really need to be deep?. Ba,Jimmy, and Rich Caruana. NIPS 2014
  2. When Does Label Smoothing Help? Müller, Rafael, Kornblith, and Hinton. NIPS 2019
  3. Towards Understanding Knowledge Distillation. Phuong, Mary and Lampert, Christoph. AAAI 2019
  4. Harnessing deep neural networks with logucal rules. ACL 2016
  5. Adaptive Regularization of Labels. Ding, Qianggang et al. arXiv:1908.05474
  6. Knowledge Isomorphism between Neural Networks. Liang, Ruofan et al. arXiv:1908.01581
  7. Role-Wise Data Augmentation for Knowledge Distillation. ICLR 2020
  8. Neural Network Distiller: A Python Package For DNN Compression Research. arXiv:1910.12232

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@gmail.com), AIDA, ECNU.

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