- Knowledge Distillation Resources: [dkozlov/awesome-knowledge-distillation].
- [2014 NeurIPS] Distilling the Knowledge in a Neural Network, [paper], [bibtex], sources: [peterliht/knowledge-distillation-pytorch], [a7b23/Distilling-the-knowledge-in-neural-network], [chengshengchan/model_compression].
- [2016 ICLR] Unifying Distillation and Privileged Information, [paper], [bibtex].
- [2017 NeurIPS] Data-Free Knowledge Distillation for Deep Neural Networks, [paper], [bibtex].
- [2018 ArXiv] Dataset Distillation, [paper], [bibtex], [homepage], sources: [SsnL/dataset-distillation].
- [2018 NeurIPS] KDGAN: Knowledge Distillation with Generative Adversarial Networks, [paper], [bibtex], [homepage], sources: [xiaojiew1/KDGAN].
- [2019 ArXiv] Data-Free Adversarial Distillation, [paper], [bibtex], sources: [VainF/Data-Free-Adversarial-Distillation].
- [2019 ICCV] Data-Free Learning of Student Networks, [paper], [bibtex], sources: [autogyro/DAFL].
- [2021 WACV] Data-free Knowledge Distillation for Object Detection, [paper], [bibtex].
- [2017 CVPR] Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach, [paper], [slides], [bibtex], sources: [giorgiop/loss-correction].
- [2017 ICML] Compressed Sensing using Generative Models, [paper], [supplementary], [bibtex], sources: [AshishBora/csgm].
- [2018 AAAI] Task-Aware Compressed Sensing with Generative Adversarial Networks, [paper], [bibtex], [homepage], sources: [po0ya/csgan].
- [2018 ICLR] AmbientGAN: Generative models from lossy measurements, [paper], [bibtex], sources: [AshishBora/ambient-gan].
- [2018 ICML] GAIN: Missing Data Imputation using Generative Adversarial Nets, [paper], [bibtex], sources: [jsyoon0823/GAIN].
- [2018 NIPS] Multivariate Time Series Imputation with Generative Adversarial Networks, [paper], [bibtex], [homepge], sources: [Luoyonghong/Multivariate-Time-Series-Imputation-with-Generative-Adversarial-Networks].
- [2019 ICLR] MisGAN: Learning from Incomplete Data with Generative Adversarial Networks, [paper], [bibtex], sources: [steveli/misgan].
- [2019 ArXiv] Improving Missing Data Imputation with Deep Generative Models, [paper], [bibtex].