PGGAN: Probability Guided Generative Adversarial Network for Image Inpainting
Bibliographic record
Abstract
Probability Guided Generative Adversarial Network (PG-GAN) aims to address key challenges in image inpainting, particularly in capturing structural information over long distances. Firstly, we design the IAModule, which provides semantic attention based on the distribution characteristics of input features, thereby enhancing semantic coherence in image inpainting. Secondly, we propose RR-SSIM Loss, a new loss function aimed at solving the problem of Structural Similarity (SSIM) that is difficult to capture long-distance structural information through sliding window calculations. Finally, we provide a new feature enhancement mechanism through channel dimension Fourier transform and design it as a HybridFFTModule. This module enhances the distinguishability of global representation through channel modeling, effectively adjusting the representation space of global information and further improving the effectiveness of image inpainting. In the experimental section, we validate the superior performance of PG-GAN on CelabA-HQ dataset. In summary, our PG-GAN provides a new and effective method for image inpainting, with broad application prospects.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".