Comments (4)
Thank you for your question.
In the training phase, we train the network to choose appropriate actions when the current states are given because we have access to the groundtruths. So, if the test images are similar enough to the train images (e.g., test and train images are from same dataset or same domain), the network can choose appropriate actions on test images even if they are unseen.
from pixelrl.
Thanks for the quick response!
So essentially, the reward calculation on the test images is redundant right? Because you can't expect to have ground truth of test images and consequently you can't define the reward. In any case, the reward on test images is not useful since you already have a trained policy and you will be following that policy irrespective of the reward you get on test image.
Please just confirm if my understanding is correct
Thank you
from pixelrl.
Yes, you are totally right. The rewards on test images are redundant and not useful.
from pixelrl.
Thanks for the clarification!
from pixelrl.
Related Issues (13)
- Image Restoration and Color Enhancement? HOT 3
- image compression using PixelRL? HOT 1
- Color images? HOT 1
- 3 channel image restoration HOT 1
- Why the FCN needs a pre-trained weight? HOT 7
- What is the moved_image used for? Can you explain the function of state.step() a bit more? HOT 3
- Have you used any pre-trained weights for the colour Enhancement part? HOT 4
- Can pixelRL be combined with GAN? HOT 3
- How to run the code in google colab
- How to run in Colab HOT 6
- Why did you augment the test set as well as the training set? And why did that boost the performance of your proposed method? HOT 1
- Reward functions used in image denoising
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from pixelrl.