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 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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".