Guang000/Awesome-Dataset-Distillation
A curated collection of research papers on dataset distillation, a deep learning technique for synthesizing compact training datasets from larger ones.
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This repository aggregates academic papers on dataset distillation, a ML technique where models trained on a small synthesized dataset achieve strong performance on the original large dataset. It documents foundational works like Wang et al.’s 2018 paper introducing the task and subsequent advances including gradient matching and applications in continual learning, privacy, and neural architecture search.
Frequently asked
- What is Guang000/Awesome-Dataset-Distillation?
- A curated collection of research papers on dataset distillation, a deep learning technique for synthesizing compact training datasets from larger ones.
- Is Awesome-Dataset-Distillation open source?
- Yes — Guang000/Awesome-Dataset-Distillation is open source, released under the MIT license.
- What language is Awesome-Dataset-Distillation written in?
- Guang000/Awesome-Dataset-Distillation is primarily written in HTML.
- How popular is Awesome-Dataset-Distillation?
- Guang000/Awesome-Dataset-Distillation has 2k stars on GitHub.
- Where can I find Awesome-Dataset-Distillation?
- Guang000/Awesome-Dataset-Distillation is on GitHub at https://github.com/Guang000/Awesome-Dataset-Distillation.