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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 121–130 of 216 papers

TitleStatusHype
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes—0
Privacy-Preserving Federated Learning via Dataset Distillation—0
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
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