# Knowledge Distillation and Data Imputation

## Knowledge Distillation
- Knowledge Distillation Resources: [[dkozlov/awesome-knowledge-distillation]](https://github.com/dkozlov/awesome-knowledge-distillation).
- [2014 NeurIPS] **Distilling the Knowledge in a Neural Network**, [[paper]](https://arxiv.org/pdf/1503.02531.pdf), [[bibtex]](/Bibtex/Distilling%20the%20Knowledge%20in%20a%20Neural%20Network.bib), sources: [[peterliht/knowledge-distillation-pytorch]](https://github.com/peterliht/knowledge-distillation-pytorch), [[a7b23/Distilling-the-knowledge-in-neural-network]](https://github.com/a7b23/Distilling-the-knowledge-in-neural-network), [[chengshengchan/model_compression]](https://github.com/chengshengchan/model_compression).
- [2016 ICLR] **Unifying Distillation and Privileged Information**, [[paper]](https://arxiv.org/pdf/1511.03643.pdf), [[bibtex]](/Bibtex/Unifying%20Distillation%20and%20Privileged%20Information.bib).
- [2017 NeurIPS] **Data-Free Knowledge Distillation for Deep Neural Networks**, [[paper]](https://arxiv.org/pdf/1710.07535.pdf), [[bibtex]](/Bibtex/Data-Free%20Knowledge%20Distillation%20for%20Deep%20Neural%20Networks.bib).
- [2018 ArXiv] **Dataset Distillation**, [[paper]](https://arxiv.org/pdf/1811.10959.pdf), [[bibtex]](/Bibtex/Dataset%20Distillation.bib), [[homepage]](https://ssnl.github.io/dataset_distillation/), sources: [[SsnL/dataset-distillation]](https://github.com/SsnL/dataset-distillation).
- [2018 NeurIPS] **KDGAN: Knowledge Distillation with Generative Adversarial Networks**, [[paper]](https://papers.nips.cc/paper/7358-kdgan-knowledge-distillation-with-generative-adversarial-networks.pdf), [[bibtex]](/Bibtex/KDGAN%20-%20Knowledge%20Distillation%20with%20Generative%20Adversarial%20Networks.bib), [[homepage]](https://papers.nips.cc/paper/7358-kdgan-knowledge-distillation-with-generative-adversarial-networks), sources: [[xiaojiew1/KDGAN]](https://github.com/xiaojiew1/KDGAN).
- [2019 ArXiv] **Data-Free Adversarial Distillation**, [[paper]](https://arxiv.org/pdf/1912.11006.pdf), [[bibtex]](/Bibtex/Data-Free%20Adversarial%20Distillation.bib), sources: [[VainF/Data-Free-Adversarial-Distillation]](https://github.com/VainF/Data-Free-Adversarial-Distillation).
- [2019 ICCV] **Data-Free Learning of Student Networks**, [[paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Data-Free_Learning_of_Student_Networks_ICCV_2019_paper.pdf), [[bibtex]](/Bibtex/Data-Free%20Learning%20of%20Student%20Networks.bib), sources: [[autogyro/DAFL]](https://github.com/autogyro/DAFL).
- [2021 WACV] **Data-free Knowledge Distillation for Object Detection**, [[paper]](https://openaccess.thecvf.com/content/WACV2021/papers/Chawla_Data-Free_Knowledge_Distillation_for_Object_Detection_WACV_2021_paper.pdf), [[bibtex]](/Bibtex/Data-free%20Knowledge%20Distillation%20for%20Object%20Detection.bib).


## Data Imputation
- [2017 CVPR] **Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach**, [[paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Patrini_Making_Deep_Neural_CVPR_2017_paper.pdf), [[slides]](https://pdfs.semanticscholar.org/d23d/684d7d413186a3cdd96cd1230d8476ea39ab.pdf), [[bibtex]](/Bibtex/Making%20Deep%20Neural%20Networks%20Robust%20to%20Label%20Noise.bib), sources: [[giorgiop/loss-correction]](https://github.com/giorgiop/loss-correction).
- [2017 ICML] **Compressed Sensing using Generative Models**, [[paper]](https://pdfs.semanticscholar.org/48a7/48193b1b30eb1191372275411a2c2fcbe5b4.pdf), [[supplementary]](http://proceedings.mlr.press/v70/bora17a/bora17a-supp.pdf), [[bibtex]](/Bibtex/Compressed%20Sensing%20using%20Generative%20Models.bib), sources: [[AshishBora/csgm]](https://github.com/AshishBora/csgm).
- [2018 AAAI] **Task-Aware Compressed Sensing with Generative Adversarial Networks**, [[paper]](https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16737/15943), [[bibtex]](/Bibtex/Task-Aware%20Compressed%20Sensing%20with%20Generative%20Adversarial%20Networks.bib), [[homepage]](http://proceedings.mlr.press/v70/bora17a.html), sources: [[po0ya/csgan]](https://github.com/po0ya/csgan).
- [2018 ICLR] **AmbientGAN: Generative models from lossy measurements**, [[paper]](https://openreview.net/pdf?id=Hy7fDog0b), [[bibtex]](/Bibtex/AmbientGAN.bib), sources: [[AshishBora/ambient-gan]](https://github.com/AshishBora/ambient-gan).
- [2018 ICML] **GAIN: Missing Data Imputation using Generative Adversarial Nets**, [[paper]](https://arxiv.org/pdf/1806.02920.pdf), [[bibtex]](/Bibtex/GAIN%20-%20Missing%20Data%20Imputation%20using%20Generative%20Adversarial%20Nets.bib), sources: [[jsyoon0823/GAIN]](https://github.com/jsyoon0823/GAIN).
- [2018 NIPS] **Multivariate Time Series Imputation with Generative Adversarial Networks**, [[paper]](https://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks.pdf), [[bibtex]](/Bibtex/Multivariate%20Time%20Series%20Imputation%20with%20Generative%20Adversarial%20Networks.bib), [[homepge]](https://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks), sources: [[Luoyonghong/Multivariate-Time-Series-Imputation-with-Generative-Adversarial-Networks]](https://github.com/Luoyonghong/Multivariate-Time-Series-Imputation-with-Generative-Adversarial-Networks).
- [2019 ICLR] **MisGAN: Learning from Incomplete Data with Generative Adversarial Networks**, [[paper]](https://openreview.net/pdf?id=S1lDV3RcKm), [[bibtex]](/Bibtex/MisGAN.bib), sources: [[steveli/misgan]](https://github.com/steveli/misgan).
- [2019 ArXiv] **Improving Missing Data Imputation with Deep Generative Models**, [[paper]](https://arxiv.org/pdf/1902.10666.pdf), [[bibtex]](/Bibtex/Improving%20Missing%20Data%20Imputation%20with%20Deep%20Generative%20Models.bib).