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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 101–125 of 216 papers

TitleStatusHype
Deep Support Vectors—0
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation—0
Distilled One-Shot Federated Learning—0
Distilling Desired Comments for Enhanced Code Review with Large Language Models—0
Distilling Long-tailed Datasets—0
Distribution-aware Dataset Distillation for Efficient Image Restoration—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
Efficient Dataset Distillation via Diffusion-Driven Patch Selection for Improved Generalization—0
Efficient Low-Resolution Face Recognition via Bridge Distillation—0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification—0
Latent Video Dataset Distillation—0
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
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
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning—0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles—0
Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation—0
Omni-supervised Facial Expression Recognition via Distilled Data—0
One Category One Prompt: Dataset Distillation using Diffusion Models—0
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