Comments (1)
StarEnhancer in the single-style enhancement experiment is trained on a multi-style dataset and tested on a single-style dataset (BasicEnhancer, on the other hand, was trained and tested on a single-style dataset). Therefore, during training, the model needs to adapt to different and randomized style embeddings.
But you are correct regarding inference. During testing, the embedding sequence used was extracted from the training images, resulting in a length of 4500. The random.sample function was then used to extract 500 samples without replacement, introducing randomness. From the experimental results, the impact of this randomness is minimal, as the average embedding of the 500 samples can still approximate the average embedding of that style quite well. The printed results (the open-source model is retrained) should be:
PSNR: 22.86 (25.41) SSIM: 0.930 (0.942) LPIPS: 0.106 (0.085)
However, from the perspective of experimental rigor, it is necessary to correct this part of the code.
Thank you very much for your question.
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Related Issues (11)
- Have you tried image retouching with StarEnhancer? HOT 6
- The results are not the same as the paper
- About pretrain model HOT 1
- How can I use style encoder to train classification on my dataset?
- Questions about dataset preparation HOT 4
- Is it possible to use a network such as VGG19 for the style encoder? HOT 4
- Is it feasible to use GAN for unpaired training? HOT 4
- Dataset processing HOT 2
- Multi-style, unpaired setting HOT 4
- How to test with my own style HOT 1
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