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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 176–200 of 216 papers

TitleStatusHype
Permutation-Invariant and Orientation-Aware Dataset Distillation for 3D Point Clouds—0
Practical Dataset Distillation Based on Deep Support Vectors—0
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives—0
Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation—0
Progressive trajectory matching for medical dataset distillation—0
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation—0
Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing—0
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective—0
Rethinking Data Distillation: Do Not Overlook Calibration—0
UDD: Dataset Distillation via Mining Underutilized Regions—0
Robust Dataset Distillation by Matching Adversarial Trajectories—0
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes—0
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring—0
Secure Federated Data Distillation—0
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator—0
Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation—0
SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching—0
Understanding Dataset Distillation via Spectral Filtering—0
Slimmable Dataset Condensation—0
A Survey on Dataset Distillation: Approaches, Applications and Future Directions—0
Mitigating Bias in Dataset Distillation—0
Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling—0
Distribution-aware Dataset Distillation for Efficient Image Restoration—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness—0
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