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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
Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset DistillationCode1
Generalizing Dataset Distillation via Deep Generative PriorCode1
DiM: Distilling Dataset into Generative ModelCode1
DREAM: Efficient Dataset Distillation by Representative MatchingCode1
Dataset Distillation with Convexified Implicit GradientsCode1
Backdoor Attacks Against Dataset DistillationCode1
Minimizing the Accumulated Trajectory Error to Improve Dataset DistillationCode1
Scaling Up Dataset Distillation to ImageNet-1K with Constant MemoryCode1
Dataset Factorization for CondensationCode1
Dataset Distillation via FactorizationCode1
Efficient Dataset Distillation Using Random Feature ApproximationCode1
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge EnvironmentsCode1
Remember the Past: Distilling Datasets into Addressable Memories for Neural NetworksCode1
Flexible Dataset Distillation: Learn Labels Instead of ImagesCode1
Soft-Label Dataset Distillation and Text Dataset DistillationCode1
Dataset DistillationCode1
Information-Guided Diffusion Sampling for Dataset Distillation—0
Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling—0
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation—0
Dataset distillation for memorized data: Soft labels can leak held-out teacher knowledgeCode0
Hyperbolic Dataset Distillation—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning—0
MGD^3: Mode-Guided Dataset Distillation using Diffusion Models—0
CONCORD: Concept-Informed Diffusion for Dataset DistillationCode0
Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation—0
Exploring Generalized Gait Recognition: Reducing Redundancy and Noise within Indoor and Outdoor DatasetsCode0
Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation—0
Leveraging Multi-Modal Information to Enhance Dataset Distillation—0
Dataset Distillation with Probabilistic Latent Features—0
Video Dataset Condensation with Diffusion Models—0
UniDetox: Universal Detoxification of Large Language Models via Dataset DistillationCode0
Latent Video Dataset Distillation—0
Distribution-aware Dataset Distillation for Efficient Image Restoration—0
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions—0
Permutation-Invariant and Orientation-Aware Dataset Distillation for 3D Point Clouds—0
Curriculum Coarse-to-Fine Selection for High-IPC Dataset DistillationCode0
Generative Dataset Distillation using Min-Max Diffusion Model—0
Enhancing Dataset Distillation via Non-Critical Region RefinementCode0
Dataset Distillation for Quantum Neural Networks—0
Finding Stable Subnetworks at Initialization with Dataset Distillation—0
Robust Dataset Distillation by Matching Adversarial Trajectories—0
Understanding Dataset Distillation via Spectral Filtering—0
Secure Federated Data Distillation—0
Does Training with Synthetic Data Truly Protect Privacy?Code0
The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions—0
Trust-Aware Diversion for Data-Effective Distillation—0
Dark Distillation: Backdooring Distilled Datasets without Accessing Raw Data—0
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential RecommendationCode0
Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training—0
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