4 papers · 1 filter
What is Dataset Distillation Learning?
William Yang, Ye Zhu, Zhiwei Deng +1
Dataset distillation has emerged as a strategy to overcome the hurdles associated with large datasets by learning a compact set of synthetic data that retains essential information…
A Label is Worth a Thousand Images in Dataset Distillation
Tian Qin, Zhiwei Deng, David Alvarez-Melis
Data is a crucial factor in the performance of machine learning models, a principle that dataset distillation methods exploit by compressing training datasets in…
Distributional Dataset Distillation with Subtask Decomposition
Tian Qin, Zhiwei Deng, David Alvarez-Melis
What does a neural network learn when training from a task-specific dataset? Synthesizing this knowledge is the central idea behind Dataset Distillation, which recent work has show…
A Sampling-Based Domain Generalization Study with Diffusion Generative Models
Ye Zhu, Yu Wu, Duo Xu +3
In this work, we investigate the domain generalization capabilities of diffusion models in the context of synthesizing images that are distinct from the training data. Instead of f…