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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 101–150 of 216 papers

TitleStatusHype
Risk of Text Backdoor Attacks Under Dataset DistillationCode0
Sequential Subset Matching for Dataset DistillationCode0
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential RecommendationCode0
Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated MatchingCode0
Towards Adversarially Robust Dataset Distillation by Curvature RegularizationCode0
Towards Mitigating Architecture Overfitting on Distilled DatasetsCode0
UniDetox: Universal Detoxification of Large Language Models via Dataset DistillationCode0
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching—0
Adaptive Dataset Quantization—0
Video Set Distillation: Information Diversification and Temporal Densification—0
Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification—0
Dataset Distillation via the Wasserstein Metric—0
FairDD: Fair Dataset Distillation via Synchronized Matching—0
Dataset Distillation Using Parameter Pruning—0
Dataset Distillation Meets Provable Subset Selection—0
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation—0
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks—0
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation—0
Few-Shot Dataset Distillation via Translative Pre-Training—0
Finding Stable Subnetworks at Initialization with Dataset Distillation—0
Dataset Distillation in Medical Imaging: A Feasibility Study—0
Privacy-Preserving Federated Learning via Dataset Distillation—0
Dataset Distillation in Latent Space—0
FocusDD: Real-World Scene Infusion for Robust Dataset Distillation—0
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning—0
FYI: Flip Your Images for Dataset Distillation—0
Dataset Distillation for Quantum Neural Networks—0
Generative Dataset Distillation: Balancing Global Structure and Local Details—0
Dataset Distillation for Medical Dataset Sharing—0
Generative Dataset Distillation Based on Self-knowledge Distillation—0
Generative Dataset Distillation using Min-Max Diffusion Model—0
Dataset Distillation for Histopathology Image Classification—0
Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory—0
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation—0
Heavy Labels Out! Dataset Distillation with Label Space Lightening—0
Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation—0
Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation—0
Hyperbolic Dataset Distillation—0
Image Dataset Compression Based on Matrix Product States—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
Importance-Aware Adaptive Dataset Distillation—0
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data—0
Dataset Distillation as Pushforward Optimal Quantization—0
Information-Guided Diffusion Sampling for Dataset Distillation—0
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions—0
Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training—0
Label-Augmented Dataset Distillation—0
Dataset Distillation: A Comprehensive Review—0
Latent Dataset Distillation with Diffusion Models—0
Latent Video Dataset Distillation—0
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