Self-Supervised Learning for Semantic Segmentation of Images
Bibliographic record
Abstract
Artificial Neural Networks (ANN) are powerful Machine Learning (ML) models that can help solve problems that are hard or even impossible to design solutions for by hand. These models learn to exploit\ninformation present in their target datasets to solve various problems. However, labelling data can be quite\nexpensive, time-consuming and often requires a domain expert. Therefore it would be quite beneficial if one\ncould train a model in such a way that exploits unlabeled data. Fortunately, Self-Supervised Learning (SSL)\nmethods are a family of learning algorithms that attempt to do just that. Many SSL methods exist, but in\nthis thesis, we explore Barlow Twins (BT) — a siamese network based on redundancy reduction, and Image\nReconstruction (IR) — a method proposed in Karnam’s thesis. In addition, we extend the Image Reconstruction method with both Coarse Cutout and Hide-and-Seek augmentations as they have been applied in similar\nsupervised and weakly-supervised segmentation task scenarios. We apply these methods and investigate the\nresults with the PASCAL VOC dataset.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".