5 papers
SAS: Semantic-aware Sampling for Generative Dataset Distillation
Mingzhuo Li, Guang Li, Linfeng Ye +4
Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational and storage costs due to large-s…
ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Zhiyuan Yang, Kai Li, Sophia Ghamoshi Ramandi +9
Computational pathology (CoPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for…
Information-Guided Diffusion Sampling for Dataset Distillation
Linfeng Ye, Shayan Mohajer Hamidi, Guang Li +3
Dataset distillation aims to create a compact dataset that retains essential information while maintaining model performance. Diffusion models (DMs) have shown promise for this tas…
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…