awesome-dataset-distillation
github.com/guang000/awesome-dataset-distillation ↗A curated list of awesome papers on dataset distillation and related applications.
1.9k
GitHub Stars
336
Curated Resources
4
Categories
6 hours ago
Last Refreshed
Latest UpdatesMainApplicationsMedia Coverage
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me better optimization resources from awesome-dataset-distillation"
Installation instructions →What's inside
Main
- Accelerating Dataset Distillation via Model AugmentationBetter Optimization
- A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and FairnessBenchmark
- A Comprehensive Survey to Dataset DistillationSurvey
- Adaptive Dataset QuantizationDataset Quantization
- A Discrepancy-Based Perspective on Dataset CondensationBetter Understanding
- A Label is Worth a Thousand Images in Dataset DistillationLabel Distillation
Applications
- A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and CondensationGraph Neural Network
- Adaptive Backdoor Attacks Against Dataset Distillation for Federated LearningPrivacy
- A Large-Scale Study on Video Action Dataset CondensationVideo
- Algorithmic Guarantees for Distilling Supervised and Offline RL DatasetsReinforcement Learning
- An Aggregation-Free Federated Learning for Tackling Data HeterogeneityFederated Learning
- An Efficient Dataset Condensation Plugin and Its Application to Continual LearningContinual Learning
Media Coverage
Showing a sample of 336 resources. View the full list on GitHub →