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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 151–175 of 216 papers

TitleStatusHype
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning—0
Leveraging Multi-Modal Information to Enhance Dataset Distillation—0
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation—0
Linear Mode Connectivity in Sparse Neural Networks—0
Data-Efficient Generation for Dataset Distillation—0
MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis—0
MetaDD: Boosting Dataset Distillation with Neural Network Architecture-Invariant Generalization—0
MGD^3: Mode-Guided Dataset Distillation using Diffusion Models—0
MIM4DD: Mutual Information Maximization for Dataset Distillation—0
Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning—0
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality—0
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning—0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles—0
Towards Universal Dataset Distillation via Task-Driven Diffusion—0
Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation—0
Dark Distillation: Backdooring Distilled Datasets without Accessing Raw Data—0
Omni-supervised Facial Expression Recognition via Distilled Data—0
Trust-Aware Diversion for Data-Effective Distillation—0
One Category One Prompt: Dataset Distillation using Diffusion Models—0
On Implicit Bias in Overparameterized Bilevel Optimization—0
On Learning Representations for Tabular Data Distillation—0
Curriculum Dataset Distillation—0
On the Size and Approximation Error of Distilled Sets—0
OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation—0
PCPs: Patient Cardiac Prototypes—0
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