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Dataset Condensation

Condense the full dataset into a tiny set of synthetic data.

Papers

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Showing 26–50 of 56 papers

TitleStatusHype
Spectral Greedy Coresets for Graph Neural Networks—0
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation—0
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation—0
Dataset Condensation via Generative Model—0
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching—0
Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation—0
No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"—0
Revisiting Permutation Symmetry for Merging Models between Different Datasets—0
Dataset Condensation with Latent Quantile Matching—0
When less is more: Simplifying inputs aids neural network understanding—0
Video Dataset Condensation with Diffusion Models—0
DCFL: Non-IID awareness Data Condensation aided Federated Learning—0
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation—0
Diffusion Dataset Condensation: Training Your Diffusion Model Faster with Less Data—0
Calibrated Dataset Condensation for Faster Hyperparameter Search—0
Medical records condensation: a roadmap towards healthcare data democratisation—0
Koopcon: A new approach towards smarter and less complex learning—0
Dataset Condensation with Latent Space Knowledge Factorization and SharingCode0
Decomposed Distribution Matching in Dataset CondensationCode0
Delving into Effective Gradient Matching for Dataset CondensationCode0
Fast Graph Condensation with Structure-based Neural Tangent KernelCode0
Mitigating Bias in Dataset DistillationCode0
Bi-Directional Multi-Scale Graph Dataset Condensation via Information BottleneckCode0
Is Adversarial Training with Compressed Datasets Effective?Code0
Bayesian Pseudo-Coresets via Contrastive DivergenceCode0
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