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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 126–150 of 216 papers

TitleStatusHype
On Implicit Bias in Overparameterized Bilevel Optimization—0
On Learning Representations for Tabular Data Distillation—0
On the Size and Approximation Error of Distilled Sets—0
OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation—0
PCPs: Patient Cardiac Prototypes—0
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
Progressive trajectory matching for medical dataset distillation—0
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation—0
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective—0
Rethinking Data Distillation: Do Not Overlook Calibration—0
Robust Dataset Distillation by Matching Adversarial Trajectories—0
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes—0
SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching—0
Slimmable Dataset Condensation—0
Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling—0
The Curse of Unrolling: Rate of Differentiating Through Optimization—0
The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions—0
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching—0
Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory—0
Towards Universal Dataset Distillation via Task-Driven Diffusion—0
Trust-Aware Diversion for Data-Effective Distillation—0
UDD: Dataset Distillation via Mining Underutilized Regions—0
Understanding Dataset Distillation via Spectral Filtering—0
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