Comments (3)
Hi, my implementation is the same as the original paper. Both learning the residual of HR and LR image. You can check the network in my implementation. Thank you!
from caffe-vdsr.
Thank you for your reply. I see where you utilize the residual now. you add a sum layer in the end which is implicitly learning the residual image.
from caffe-vdsr.
Hi, sorry to bother you again. I am having some difficulties to achieve the same performance as you did in model_170000. If you don't mind, is there anyway that I can talk with you privately for the matter?
from caffe-vdsr.
Related Issues (20)
- Multi-Scale Implementation HOT 10
- Caffemodels corresponding to VDSR_official.mat and VDSR_ADAM.mat HOT 3
- This version isn't use the clip-gradients? HOT 2
- about the data sample HOT 3
- loss during training HOT 9
- Test Function at Caffe HOT 2
- Error in VDSR_Matconvnet (line 15) HOT 4
- A display bug in Demo_SR_Conv.m ?
- how to get high resolution output by using my own image data?
- Questions about parameters HOT 2
- sr_psnr less than bicubic psnr HOT 6
- Test code in pyCaffe/C++ HOT 7
- About learning rate HOT 2
- File for training data is offline – alternative location? HOT 2
- Data Preprocessing HOT 2
- About GPU load
- loss nan
- Thanks and some questions HOT 1
- Questions about usage HOT 1
- Some questions about DEM(>255)
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from caffe-vdsr.