{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dataset-distillation-with-convexified","title":"Dataset Distillation with Convexified Implicit Gradients","arxiv_id":"2302.06755","date":"2023-02-13","proceeding":null,"authors":["Noel Loo","Ramin Hasani","Mathias Lechner","Daniela Rus"],"abstract":"We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art. To this end, we first formulate dataset distillation as a bi-level optimization problem. Then, we show how implicit gradients can be effectively used to compute meta-gradient updates. We further equip the algorithm with a convexified approximation that corresponds to learning on top of a frozen finite-width neural tangent kernel. Finally, we improve bias in implicit gradients by parameterizing the neural network to enable analytical computation of final-layer parameters given the body parameters. RCIG establishes the new state-of-the-art on a diverse series of dataset distillation tasks. Notably, with one image per class, on resized ImageNet, RCIG sees on average a 108\\% improvement over the previous state-of-the-art distillation algorithm. Similarly, we observed a 66\\% gain over SOTA on Tiny-ImageNet and 37\\% on CIFAR-100.","url_abs":"https://arxiv.org/abs/2302.06755v2","url_pdf":"https://arxiv.org/pdf/2302.06755v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dataset-distillation-with-convexified","repo_url":"https://github.com/yolky/rcig","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok"}},{"paper_slug":"dataset-distillation-with-convexified","repo_url":"https://github.com/Guang000/Awesome-Dataset-Distillation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"dataset-distillation","task_name":"Dataset Distillation"},{"task_slug":"dataset-distillation-1ipc","task_name":"Dataset Distillation - 1IPC"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2302.06755","atlas_url":"https://app.syntology.ai/?focus=2302.06755","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}