SIR-SRGAN-ResNeXt: A New Super-Resolution GAN with Self-Interpolation Ranker and ResNeXt Generator
Bibliographic record
Abstract
Super-resolution can be a powerful tool in enhancing image quality and bringing clarity to image details. It is essential for various fields such as medical and surveillance imaging as it improves image resolution to reveal fine details. Super-Resolution Generative Adversarial Networks (SRGAN) have shown promising capabilities toward perfecting super-resolution. However, the SRGAN with Self-Interpolation Ranker (SIR-SRGAN), using the difference between the reconstructed and the original image, often produces images with blurred areas, compromising image quality. This paper introduces SIR-SRGAN-ResNeXt, an improved version of SIR-SRGAN capable of generating clearer images with higher-quality metrics. The proposed model retains the Self-Interpolation classifier of SIR-SRGAN, incorporates a U-net-based discriminator, and adds attention layers for more effective feature analysis. Moreover, the generator is shifted to a more complex ResNeXt-based model, resulting in improved performance when evaluated against state-of-the-art SRGAN models in terms of high resolution and optimal output file size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".