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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 76–100 of 216 papers

TitleStatusHype
Dataset Distillation in Latent Space—0
Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation—0
Efficient Dataset Distillation via Diffusion-Driven Patch Selection for Improved Generalization—0
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning—0
A Comprehensive Survey of Dataset Distillation—0
Finding Stable Subnetworks at Initialization with Dataset Distillation—0
FocusDD: Real-World Scene Infusion for Robust Dataset Distillation—0
Dataset Distillation for Quantum Neural Networks—0
Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing—0
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation—0
Adaptive Dataset Quantization—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
Dataset Distillation for Medical Dataset Sharing—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
Dataset Distillation for Histopathology Image Classification—0
Distilling Long-tailed Datasets—0
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring—0
Distilling Desired Comments for Enhanced Code Review with Large Language Models—0
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation—0
Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification—0
Distilled One-Shot Federated Learning—0
A Survey on Dataset Distillation: Approaches, Applications and Future Directions—0
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