1 research outputs found
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis
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 time. The increase of training time is also not
conducive to launch application of SR algorithms. This's why we abandoned
DenseNet as basic network. Futhermore, we abandoned this paper due to its
limitation only applied on medical images. Please view our lastest work applied
on general images at arXiv:1911.03464.Comment: We abandoned this paper due to its limitation only applied on medical
images, please view our lastest work at arXiv:1911.0346