5 papers
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios
Kai Wang, Zekai Li, Zhi-Qi Cheng +6
Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios.…
ATOM: Attention Mixer for Efficient Dataset Distillation
Samir Khaki, Ahmad Sajedi, Kai Wang +3
Recent works in dataset distillation seek to minimize training expenses by generating a condensed synthetic dataset that encapsulates the information present in a larger real datas…
DataDAM: Efficient Dataset Distillation with Attention Matching
Ahmad Sajedi, Samir Khaki, Ehsan Amjadian +3
Researchers have long tried to minimize training costs in deep learning while maintaining strong generalization across diverse datasets. Emerging research on dataset distillation a…
Data-to-Model Distillation: Data-Efficient Learning Framework
Ahmad Sajedi, Samir Khaki, Lucy Z. Liu +3
Dataset distillation aims to distill the knowledge of a large-scale real dataset into small yet informative synthetic data such that a model trained on it performs as well as a mod…
Prioritize Alignment in Dataset Distillation
Zekai Li, Ziyao Guo, Wangbo Zhao +8
Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this,…