7 citations · 8 across the 3 of their papers we have counts for
4 papers
Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks
Yuchong Yao, Xiaohui Wangr, Yuanbang Ma +5
Class imbalance occurs in many real-world applications, including image classification, where the number of images in each class differs significantly. With imbalanced data, the ge…
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks
Yuan Ma, Kewen Liu, Hongxia Xiong +4
The presence of residual and dense neural networks which greatly promotes the development of image Super-Resolution(SR) have witnessed a lot of impressive results. Depending on our…
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis
Kewen Liu, Yuan Ma, Hongxia Xiong +6
During training phase, more connections (e.g. channel concatenation in last layer of DenseNet) means more occupied GPU memory and lower GPU utilization, requiring more training tim…
Medical image super-resolution method based on dense blended attention network
Kewen Liu, Yuan Ma, Hongxia Xiong +4
In order to address the issue that medical image would suffer from severe blurring caused by the lack of high-frequency details in the process of image super-resolution reconstruct…