MSG-CapsGAN: Multi-Scale Gradient Capsule GAN for Face Super Resolution
Why this work is in the frame
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Bibliographic record
Abstract
One of the most useful sub-fields of Super-Resolution (SR) is face SR. Given a Low-Resolution (LR) image of a face, the High-Resolution (HR) counterpart is demanded. However, performing SR task on extremely low resolution images is very challenging due to the image distortion in the HR results. Many deep learning-based SR approaches have intended to solve this issue by using attribute domain information. However, they require more complex data and even additional networks. To simplify this process and yet preserve the precision, a novel Multi-Scale Gradient GAN with Capsule Network as its discriminator is proposed in this paper. MSG-CapsGAN surpassed the state-of-the-art face SR networks in terms of PSNR. This network is a step towards a precise pose invariant SR system.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 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