A Positive-Unlabeled Generative Adversarial Network for Super-Resolution Image Reconstruction Using a Charbonnier Loss
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Bibliographic record
Abstract
Recently, the generative adversarial network (GAN) has been widely used to obtain the real high-frequency details of images. This spurs the application of GAN in super-resolution reconstruction. However, GAN is unstable in the training process, for the following two reasons: Firstly, the discriminator in GAN keeps the positive (true) and negative (false) criteria of the generated samples unchanged throughout the learning process, without considering the gradual quality improvement of the generated samples (Sometimes, the generated samples are even more realistic than the real samples). To solve the above problems, this paper proposes a super-resolution model based on positive-unlabeled (PU)-GAN-Charbon (SRPUGAN-Charbon). The proposed model includes one generator network that synthetizes super-resolution images and one discriminator network trained to distinguish super-resolution images from real high-resolution images. In addition, the Charbonnier loss function was called to handle the outliers in super-resolution images, and retain the low-frequency features of super-resolution images. Extensive experiments were conducted on three benchmark databases, including BSDS500, Set5, and Set14. The results show that the proposed SRPUGAN-Charbon method is superior to the most advanced methods in terms of visual effect, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it