{"type":"ScholarlyArticle","title":"A Survey on Dataset Distillation: Approaches, Applications and Future Directions","authors":[{"name":"Jiahui Geng","slug":"jiahui-geng","given":"Jiahui","family":"Geng","affiliation":"University of Stavanger"},{"name":"Zongxiong Chen","slug":"zongxiong-chen","given":"Zongxiong","family":"Chen","affiliation":"Fraunhofer FOKUS"},{"name":"Yuandou Wang","slug":"yuandou-wang","given":"Yuandou","family":"Wang","affiliation":"University of Amsterdam"},{"name":"Herbert Woisetschlaeger","slug":"herbert-woisetschlaeger","given":"Herbert","family":"Woisetschlaeger","affiliation":"Technical University of Munich"},{"name":"Sonja Schimmler","slug":"sonja-schimmler","given":"Sonja","family":"Schimmler","affiliation":"Fraunhofer FOKUS"},{"name":"Ruben Mayer","slug":"ruben-mayer","given":"Ruben","family":"Mayer","affiliation":"Technical University of Munich"},{"name":"Zhiming Zhao","slug":"zhiming-zhao","given":"Zhiming","family":"Zhao","affiliation":"University of Amsterdam"},{"name":"Chunming Rong","slug":"chunming-rong","given":"Chunming","family":"Rong","affiliation":"University of Stavanger"}],"abstract":"Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high. By synthesizing datasets with high information density, dataset distillation offers a range of potential applications, including support for continual learning, neural architecture search, and privacy protection. Despite recent advances, we lack a holistic understanding of the approaches and applications. Our survey aims to bridge this gap by first proposing a taxonomy of dataset distillation, characterizing existing approaches, and then systematically reviewing the data modalities, and related applications. In addition, we summarize the challenges and discuss future directions for this field of research.","doi":"10.24963/ijcai.2023/741","url":"https://ijcai.org/proceedings/2023/741","pdf":"https://ijcai.org/proceedings/2023/0741.pdf","markdown":"https://ijcai.org/proceedings/2023/741.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/741","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Survey Track","track":"Survey Track","number":741,"pages":{"first":6610,"last":6618},"keywords":["Survey: AI Ethics, Trust, Fairness","Survey: Knowledge Representation and Reasoning","Survey: Machine Learning","Survey: Computer Vision"],"videos":[],"references":[{"position":1,"raw":"[Abdal et al., 2019] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan: How to embed images into the stylegan latent space? In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 4432- 4441, 2019.","title":"Image2stylegan: How to embed images into the stylegan latent space?","authors":"Rameen Abdal, Yipeng Qin, Peter Wonka","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Andrychowicz et al., 2016] Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems, 29, 2016.","title":"Learning to learn by gradient descent by gradient descent","authors":"Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando De Freitas","year":"2016","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.","title":"Neural machine translation by jointly learning to align and translate","authors":"Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio","year":"2015","venue":"3rd International Conference on Learning Representations, ICLR 2015","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Carlini et al., 2023] Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, and Eric Wallace. Extract- ing training data from diffusion models. arXiv preprint arXiv:2301.13188, 2023.","title":"Extracting training data from diffusion models","authors":"Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Castro et al., 2018] Francisco M Castro, Manuel J Marín- Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Ala- hari. End-to-end incremental learning. In Proceedings of the European conference on computer vision (ECCV), pages 233-248, 2018.","title":"End-to-end incremental learning","authors":"Castro","year":"2018","venue":"Proceedings of the European conference on computer vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cazenavette et al., 2022] George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. Dataset distillation by matching training trajectories. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4750-4759, 2022.","title":"Dataset distillation by matching training trajectories","authors":"George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, Jun-Yan Zhu","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2022] Dingfan Chen, Raouf Kerkouche, and Mario Fritz. Private set generation with discriminative in- formation. arXiv preprint arXiv:2211.04446, 2022.","title":"Private set generation with discriminative information","authors":"Chen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Cui et al., 2022] Justin Cui, Ruochen Wang, Si Si, and Cho- Jui Hsieh. Dc-bench: Dataset condensation benchmark. arXiv preprint arXiv:2207.09639, 2022.","title":"Dc-bench: Dataset condensation benchmark","authors":"Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Deng et al., 2009] Jia Deng, Wei Dong, Richard Socher, Li- Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. Ieee, 2009.","title":"Imagenet: A large-scale hierarchical image database","authors":"Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei","year":"2009","venue":"IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Deng, 2022] Olga Deng, Zhiwei; Russakovsky. Remember the past: Distilling datasets into addressable memories for neural networks. arXiv preprint arXiv:2206.02916, 2022.","title":"Remember the past: Distilling datasets into addressable memories for neural networks","authors":"Olga Deng, ; Zhiwei, Russakovsky","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dong et al., 2022] Tian Dong, Bo Zhao, and Lingjuan Lyu. Privacy for free: How does dataset condensation help pri- vacy? arXiv preprint arXiv:2206.00240, 2022.","title":"Privacy for free: How does dataset condensation help privacy","authors":"Tian Dong, Bo Zhao, Lingjuan Lyu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Dwork, 2006] Cynthia Dwork. Differential privacy. In Au- tomata, Languages and Programming: 33rd International Colloquium, ICALP 2006, Venice, Italy, July 10-14, 2006, Proceedings, Part II 33, pages 1-12. Springer, 2006.","title":"Differential privacy","authors":"Cynthia Dwork","year":"2006","venue":"Automata, Languages and Programming: 33rd International Colloquium, ICALP 2006","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Fredrikson et al., 2015] Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC conference on com- puter and communications security, pages 1322-1333, 2015.","title":"Model inversion attacks that exploit confidence information and basic countermeasures","authors":"Matt Fredrikson, Somesh Jha, Thomas Ristenpart","year":"2015","venue":"Proceedings of the 22nd ACM SIGSAC conference on computer and communications security","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Geng et al., 2021] Jiahui Geng, Yongli Mou, Feifei Li, Qing Li, Oya Beyan, Stefan Decker, and Chunming Rong. To- wards general deep leakage in federated learning. arXiv preprint arXiv:2110.09074, 2021.","title":"Towards general deep leakage in federated learning","authors":"Jiahui Geng, Yongli Mou, Feifei Li, Qing Li, Oya Beyan, Stefan Decker, Chunming Rong","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Geng et al., 2023] Jiahui Geng, Yongli Mou, Qing Li, Feifei Li, Oya Beyan, Stefan Decker, and Chunming Rong. Im- proved gradient inversion attacks and defenses in federated learning. IEEE Transactions on Big Data, 2023.","title":"Improved gradient inversion attacks and defenses in federated learning","authors":"Jiahui Geng, Yongli Mou, Qing Li, Feifei Li, Oya Beyan, Stefan Decker, Chunming Rong","year":"2023","venue":"IEEE Transactions on Big Data","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Graves et al., 2017] Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu. Auto- mated curriculum learning for neural networks. In interna- tional conference on machine learning, pages 1311-1320. PMLR, 2017.","title":"Automated curriculum learning for neural networks","authors":"Alex Graves, Jacob Marc G Bellemare, Remi Menick, Koray Munos, Kavukcuoglu","year":"2017","venue":"international conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Hu et al., 2022] Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, and Yue Liu. Fedsynth: Gra- dient compression via synthetic data in federated learning. arXiv preprint arXiv:2204.01273, 2022.","title":"Fedsynth: Gradient compression via synthetic data in federated learning","authors":"Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, Yue Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Jacot et al., 2018] Arthur Jacot, Franck Gabriel, and Clément Hongler. Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural information processing systems, 31, 2018.","title":"Neural tangent kernel: Convergence and generalization in neural networks","authors":"Arthur Jacot, Franck Gabriel, Clément Hongler","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Jin et al., 2021] Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah. Graph con- densation for graph neural networks. arXiv preprint arXiv:2110.07580, 2021.","title":"Graph condensation for graph neural networks","authors":"Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Jin et al., 2022] Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, and Bing Yin. Condensing graphs via one-step gradient matching. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 720-730, 2022.","title":"Condensing graphs via one-step gradient matching","authors":"Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bing Yin","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Kim et al., 2022] Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung- Woo Ha, and Hyun Oh Song. Dataset condensation via efficient synthetic-data parameterization. arXiv preprint arXiv:2205.14959, 2022.","title":"Dataset condensation via efficient synthetic-data parameterization","authors":"Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Koehn, 2005] Philipp Koehn. Europarl: A parallel corpus for statistical machine translation. In Proceedings of Ma- chine Translation Summit X: Papers, pages 79-86, Phuket, Thailand, September 13-15 2005.","title":"Europarl: A parallel corpus for statistical machine translation","authors":"Philipp Koehn","year":"2005","venue":"Proceedings of Machine Translation Summit X: Papers","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Konyushkova et al., 2017] Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua. Learning active learning from data. Advances in neural information processing systems, 30, 2017.","title":"Learning active learning from data","authors":"Ksenia Konyushkova, Raphael Sznitman, Pascal Fua","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lee et al., 2022] Hae Beom Lee, Dong Bok Lee, and Sung Ju Hwang. Dataset condensation with latent space knowledge factorization and sharing. arXiv preprint arXiv:2208.10494, 2022.","title":"Dataset condensation with latent space knowledge factorization and sharing","authors":"Hae Beom, Lee, Dong Bok Lee, Sung Ju Hwang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2022] Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama. Dataset distillation for medical dataset sharing. arXiv preprint arXiv:2209.14603, 2022.","title":"Dataset distillation for medical dataset sharing","authors":"Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2022a] Mengyang Liu, Shanchuan Li, Xinshi Chen, and Le Song. Graph condensation via receptive field distribution matching. arXiv preprint arXiv:2206.13697, 2022.","title":"Graph condensation via receptive field distribution matching","authors":"Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2022b] Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang. Dataset distillation via factorization. arXiv preprint arXiv:2210.16774, 2022.","title":"Dataset distillation via factorization","authors":"Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Liu et al., 2023] Yugeng Liu, Zheng Li, Michael Backes, Yun Shen, and Yang Zhang. Backdoor attacks against dataset distillation. arXiv preprint arXiv:2301.01197, 2023.","title":"Backdoor attacks against dataset distillation","authors":"Yugeng Liu, Zheng Li, Michael Backes, Yun Shen, Yang Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Loo et al., 2022] Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus. Efficient dataset distillation using random feature approximation. arXiv preprint arXiv:2210.12067, 2022.","title":"Efficient dataset distillation using random feature approximation","authors":"Noel Loo, Ramin Hasani, Alexander Amini, Daniela Rus","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Maclaurin et al., 2015] Dougal Maclaurin, David Duve- naud, and Ryan Adams. Gradient-based hyperparame- ter optimization through reversible learning. In Interna- tional conference on machine learning, pages 2113-2122. PMLR, 2015.","title":"Gradient-based hyperparameter optimization through reversible learning","authors":"Dougal Maclaurin, David Duvenaud, Ryan Adams","year":"2015","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Nguyen et al., 2020] Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. Dataset meta-learning from kernel ridge- regression. arXiv preprint arXiv:2011.00050, 2020.","title":"Dataset meta-learning from kernel ridgeregression","authors":"Timothy Nguyen, Zhourong Chen, Jaehoon Lee","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Nguyen et al., 2021] Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee. Dataset distillation with infinitely wide convolutional networks. Advances in Neu- ral Information Processing Systems, 34:5186-5198, 2021.","title":"Dataset distillation with infinitely wide convolutional networks","authors":"Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Platt and others, 1999] John Platt et al. Probabilistic outputs for support vector machines and comparisons to regular- ized likelihood methods. Advances in large margin classi- fiers, 10(3):61-74, 1999.","title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","authors":"John Platt","year":"1999","venue":"Advances in large margin classifiers","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Prabhu et al., 2020] Ameya Prabhu, Philip HS Torr, and Puneet K Dokania. Gdumb: A simple approach that ques- tions our progress in continual learning. In European conference on computer vision, pages 524-540. Springer, 2020.","title":"Gdumb: A simple approach that questions our progress in continual learning","authors":"Ameya Prabhu, Puneet K Philip Hs Torr, Dokania","year":"2020","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Schmidhuber, 2015] Jürgen Schmidhuber. Deep learning in neural networks: An overview. Neural networks, 61:85- 117, 2015.","title":"Deep learning in neural networks: An overview","authors":"Jürgen Schmidhuber","year":"2015","venue":"Neural networks","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Sener and Savarese, 2017] Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core- set approach. arXiv preprint arXiv:1708.00489, 2017.","title":"Active learning for convolutional neural networks: A coreset approach","authors":"Ozan Sener, Silvio Savarese","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Shokri et al., 2017] Reza Shokri, Marco Stronati, Con- gzheng Song, and Vitaly Shmatikov. Membership infer- ence attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3- 18. IEEE, 2017.","title":"Membership inference attacks against machine learning models","authors":"Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov","year":"2017","venue":"IEEE symposium on security and privacy (SP)","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Such et al., 2020] Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune. Genera- tive teaching networks: Accelerating neural architecture search by learning to generate synthetic training data. In International Conference on Machine Learning, pages 9206-9216. PMLR, 2020.","title":"Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data","authors":"Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, Jeffrey Clune","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Sucholutsky and Schonlau, 2021] Ilia Sucholutsky and Matthias Schonlau. Soft-label dataset distillation and text dataset distillation. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2021.","title":"Ilia Sucholutsky and Matthias Schonlau. Soft-label dataset distillation and text dataset distillation","authors":"Schonlau Sucholutsky","year":"2021","venue":"2021 International Joint Conference on Neural Networks (IJCNN)","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Tsilivis et al., 2022] Nikolaos Tsilivis, Jingtong Su, and Ju- lia Kempe. Can we achieve robustness from data alone? arXiv preprint arXiv:2207.11727, 2022.","title":"Can we achieve robustness from data alone","authors":"Nikolaos Tsilivis, Jingtong Su, Julia Kempe","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wang et al., 2018] Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. Dataset distillation. arXiv preprint arXiv:1811.10959, 2018.","title":"Dataset distillation","authors":"Wang","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Wang et al., 2022] Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You. Cafe: Learning to condense dataset by aligning features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 12196-12205, 2022.","title":"Cafe: Learning to condense dataset by aligning features","authors":"Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, Yang You","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Warden, 2018] Pete Warden. Speech commands: A dataset for limited-vocabulary speech recognition. arXiv preprint arXiv:1804.03209, 2018.","title":"Speech commands: A dataset for limited-vocabulary speech recognition","authors":"Pete Warden","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Wu et al., 2022] Yihan Wu, Xinda Li, Florian Kerschbaum, Heng Huang, and Hongyang Zhang. Towards robust dataset learning. arXiv preprint arXiv:2211.10752, 2022.","title":"Towards robust dataset learning","authors":"Yihan Wu, Xinda Li, Florian Kerschbaum, Heng Huang, Hongyang Zhang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Xiong et al., 2022] Yuanhao Xiong, Ruochen Wang, Min- hao Cheng, Felix Yu, and Cho-Jui Hsieh. Feddm: Iterative distribution matching for communication-efficient feder- ated learning. arXiv preprint arXiv:2207.09653, 2022.","title":"Feddm: Iterative distribution matching for communication-efficient federated learning","authors":"Xiong","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhao and Bilen, 2021] Bo Zhao and Hakan Bilen. Dataset condensation with distribution matching. ArXiv, abs/2110.04181, 2021.","title":"Dataset condensation with distribution matching","authors":"Bo Zhao, Hakan Bilen","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhao and Bilen, 2022] Bo Zhao and Hakan Bilen. Syn- thesizing informative training samples with gan. arXiv preprint arXiv:2204.07513, 2022.","title":"Synthesizing informative training samples with gan","authors":"Bo Zhao, Hakan Bilen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhao et al., 2021] Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. Dataset condensation with gradient match- ing. ICLR, 1(2):3, 2021.","title":"Dataset condensation with gradient matching","authors":"Bo Zhao, Konda Reddy Mopuri, Hakan Bilen","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Zhao Bo, 2021] Bilen Hakan Zhao Bo. Dataset condensa- tion with differentiable siamese augmentation. In Inter- national Conference on Machine Learning, pages 12674- 12685. PMLR, 2021.","title":"Dataset condensation with differentiable siamese augmentation","authors":"Bilen Hakan, Zhao Bo","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhou et al., 2022] Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba. Dataset distillation using neural feature regres- sion. arXiv preprint arXiv:2206.00719, 2022.","title":"Dataset distillation using neural feature regression","authors":"Zhou","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}