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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
Towards Universal Dataset Distillation via Task-Driven Diffusion—0
Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation—0
A Large-Scale Study on Video Action Dataset CondensationCode1
Distilling Desired Comments for Enhanced Code Review with Large Language Models—0
Adaptive Dataset Quantization—0
Efficient Dataset Distillation via Diffusion-Driven Patch Selection for Improved Generalization—0
Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual InformationCode0
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation—0
DELT: A Simple Diversity-driven EarlyLate Training for Dataset DistillationCode1
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
Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based PruningCode0
Dataset Distillers Are Good Label Denoisers In the WildCode0
Color-Oriented Redundancy Reduction in Dataset DistillationCode0
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
Emphasizing Discriminative Features for Dataset Distillation in Complex ScenariosCode1
Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?Code1
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
MetaDD: Boosting Dataset Distillation with Neural Network Architecture-Invariant Generalization—0
Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep NetworksCode0
Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight AdjustmentCode0
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data—0
Label-Augmented Dataset Distillation—0
Efficient Low-Resolution Face Recognition via Bridge Distillation—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
Data-Efficient Generation for Dataset Distillation—0
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning—0
Neural Spectral Decomposition for Dataset DistillationCode0
UDD: Dataset Distillation via Mining Underutilized Regions—0
Distilling Long-tailed DatasetsCode0
Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation—0
Dataset Distillation for Histopathology Image Classification—0
Generative Dataset Distillation Based on Diffusion ModelCode1
Heavy Labels Out! Dataset Distillation with Label Space Lightening—0
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator—0
Prioritize Alignment in Dataset DistillationCode1
MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis—0
Dataset Distillation for Offline Reinforcement LearningCode0
D^4M: Dataset Distillation via Disentangled Diffusion ModelCode1
Dataset Distillation in Medical Imaging: A Feasibility Study—0
Dataset Distillation by Automatic Training TrajectoriesCode0
DDFAD: Dataset Distillation Framework for Audio Data—0
FYI: Flip Your Images for Dataset Distillation—0
Dataset Quantization with Active Learning based Adaptive SamplingCode1
Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory—0
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