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Dataset Distillation

Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset. A dataset distillation algorithm takes as input a large real dataset to be distilled (training set), and outputs a small synthetic distilled dataset, which is evaluated via testing models trained on this distilled dataset on a separate real dataset (validation/test set). A good small distilled dataset is not only useful in dataset understanding, but has various applications (e.g., continual learning, privacy, neural architecture search, etc.).

Papers

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Showing 201–216 of 216 papers

TitleStatusHype
Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
FairDD: Fair Dataset Distillation via Synchronized Matching—0
Distribution-aware Dataset Distillation for Efficient Image Restoration—0
Distilling Desired Comments for Enhanced Code Review with Large Language Models—0
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness—0
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks—0
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
Finding Stable Subnetworks at Initialization with Dataset Distillation—0
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation—0
Privacy-Preserving Federated Learning via Dataset Distillation—0
Deep Support Vectors—0
FocusDD: Real-World Scene Infusion for Robust Dataset Distillation—0
DDFAD: Dataset Distillation Framework for Audio Data—0
FYI: Flip Your Images for Dataset Distillation—0
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