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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
On Learning Representations for Tabular Data Distillation—0
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring—0
Dataset Distillation as Pushforward Optimal Quantization—0
FocusDD: Real-World Scene Infusion for Robust Dataset Distillation—0
Generative Dataset Distillation Based on Self-knowledge Distillation—0
Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation—0
OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation—0
Towards Universal Dataset Distillation via Task-Driven Diffusion—0
Distilling Desired Comments for Enhanced Code Review with Large Language Models—0
Adaptive Dataset Quantization—0
Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual InformationCode0
Efficient Dataset Distillation via Diffusion-Driven Patch Selection for Improved Generalization—0
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation—0
FairDD: Fair Dataset Distillation via Synchronized Matching—0
Video Set Distillation: Information Diversification and Temporal Densification—0
Data-to-Model Distillation: Data-Efficient Learning FrameworkCode0
Color-Oriented Redundancy Reduction in Dataset DistillationCode0
Dataset Distillers Are Good Label Denoisers In the WildCode0
Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based PruningCode0
BEARD: Benchmarking the Adversarial Robustness for Dataset DistillationCode0
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes—0
Privacy-Preserving Federated Learning via Dataset Distillation—0
Risk of Text Backdoor Attacks Under Dataset DistillationCode0
Enhancing Dataset Distillation via Label Inconsistency Elimination and Learning Pattern RefinementCode0
Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated MatchingCode0
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