Improved Semi-Supervised Attention GAN for Semantic Segmentation
Bibliographic record
Abstract
Semantic segmentation is one of the cornerstone problems in computer vision that involves assigning each image pixel to a specific semantic class. Traditional supervised learning approaches are heavily dependent on labeled data, which is often costly and time-consuming to obtain. Semi-supervised learning approaches, on the other hand, offer a promising path to improve segmentation accuracy by combining labeled and unlabeled data. This work uses an attention-driven adversarial training strategy-based generative adversarial network (GAN) to create realistic semantic segmentation maps for unlabeled data while enhancing segmentation accuracy for labeled data. Additionally, it introduces a patch-wise discriminator to extract rich contextual information. Extensive ablation studies on two widely adopted datasets, Cityscapes and CamVid, demonstrate the effectiveness of the proposed model, achieving state-of-the-art performances. It contributes to the advancement of semi-supervised learning in semantic segmentation, providing a practical solution for improving segmentation accuracy while reducing the reliance on labeled data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".