332 citations · 433 across the 20 of their papers we have counts for
9 papers · 1 filter
X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models
Yixiong Chen, Li Liu, Chris Ding
This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation m…
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
Yixiong Chen, Chunhui Zhang, Chris H. Q. Ding +1
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical dataset…
Rethinking Two Consensuses of the Transferability in Deep Learning
Yixiong Chen, Jingxian Li, Chris Ding +1
Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans. This ability is name…
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
Yixiong Chen, Li Liu, Jingxian Li +3
In medical image analysis, transfer learning is a powerful method for deep neural networks (DNNs) to generalize well on limited medical data. Prior efforts have focused on developi…
Revisiting L21-norm Robustness with Vector Outlier Regularization
Bo Jiang, Chris Ding
In many real-world applications, data usually contain outliers. One popular approach is to use L2,1 norm function as a robust error/loss function. However, the robustness of L2,1 n…
L1-norm Error Function Robustness and Outlier Regularization
Chris Ding, Bo Jiang
In many real-world applications, data come with corruptions, large errors or outliers. One popular approach is to use L1-norm function. However, the robustness of L1-norm function…