Counterfactual Attention for Facial Image Super-Resolution
Bibliographic record
Abstract
Super-resolution (SR) is the task of recovering High-Resolution (HR) images from given Low- Resolution (LR) Images. Various SR methods are available in the literature. The attention mechanism is one of the widely used approaches in the field of SR. In this paper, Counterfactual Attention Learning (CAL) based on causal inference is applied to increase the quality of attention in Face Super-Resolution (FSR). This approach helps to assess the quality of attention and provides a strong signal to supervise the learning activity. The paper discusses the effect of the learned attention on the task of SR through counterfactual intervention and the effect is maximized to make the model learn useful attention for FSR. The effectiveness of the method is tested, and upscaling is achieved using the Scale-Arbitrary SR model (ArbSR), which can handle both integer and non-integer scale factors. The experiments are carried out for different scale factors on the CelebA dataset. The results show that the technique enhances the performance of FSR task by both quality of the image and PSNR.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".