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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 51–100 of 216 papers

TitleStatusHype
A Label is Worth a Thousand Images in Dataset DistillationCode1
Flexible Dataset Distillation: Learn Labels Instead of ImagesCode1
Can pre-trained models assist in dataset distillation?Code1
On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation ParadigmCode1
Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory MatchingCode1
Dataset Distillation via FactorizationCode1
D^4M: Dataset Distillation via Disentangled Diffusion ModelCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
Dataset Distillation via Vision-Language Category PrototypeCode1
Dataset Distillation with Convexified Implicit GradientsCode1
Generalizing Dataset Distillation via Deep Generative PriorCode1
Distilling Datasets Into Less Than One ImageCode1
DataDAM: Efficient Dataset Distillation with Attention MatchingCode1
Embarassingly Simple Dataset DistillationCode1
Dataset Factorization for CondensationCode1
Group Distributionally Robust Dataset Distillation with Risk MinimizationCode1
Dark Distillation: Backdooring Distilled Datasets without Accessing Raw Data—0
FYI: Flip Your Images for Dataset Distillation—0
Dataset Distillation via the Wasserstein Metric—0
Generative Dataset Distillation: Balancing Global Structure and Local Details—0
Curriculum Dataset Distillation—0
Dataset Distillation Using Parameter Pruning—0
Privacy-Preserving Federated Learning via Dataset Distillation—0
Dataset Distillation Meets Provable Subset Selection—0
Dataset Distillation in Medical Imaging: A Feasibility Study—0
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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