16 citations · 90 across the 26 of their papers we have counts for
4 papers · 1 filter
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.…
More Than Positive and Negative: Communicating Fine Granularity in Medical Diagnosis
Xiangyu Peng, Kai Wang, Jianfei Yang +2
With the advance of deep learning, much progress has been made in building powerful artificial intelligence (AI) systems for automatic Chest X-ray (CXR) analysis. Most existing AI…
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,…
Must: Maximizing Latent Capacity of Spatial Transcriptomics Data
Zelin Zang, Liangyu Li, Yongjie Xu +5
Spatial transcriptomics (ST) technologies have revolutionized the study of gene expression patterns in tissues by providing multimodality data in transcriptomic, spatial, and morph…