Bibliographic record
Abstract
In recent years, convolutional neural networks (CNNs) are leading the way in many computer vision problems. Since the development of fully convolutional networks, CNNs have been widely employed for low-level pixel-labeling problems, and successfully pushed the performance to a new level. Although CNNs are able to extract highly discriminative features, they typically assign a class label to each image pixel individually. This leads to various spatial inconsistencies. Therefore, CNNs are commonly combined with graphical models, such as conditional random fields (CRFs), to impose spatial coherence. CRFs were invented precisely for the task of imposing spatial coherence among image pixels. The coherence regularization weight serves an important role of controlling the regularization strength in the CRF optimization, and has a great influence on the quality of the final result. Traditionally this weight value is set to a fixed number for all images.\nIn this thesis, we propose a novel approach to learn the coherence regularization weight for each individual image using a CNN, and then apply this per-image-learned weight in the CNN+CRF system. We first construct a dataset where the optimal regularization weight for the CRF optimization has been pre-computed for each image. We adopt convolutional regression networks with standard architecture for learning, and tailor the input according to our problem. We test the effectiveness of our approach on the task of salient object segmentation where a graph-cut based CRF optimizer can generate globally optimal solution. We show that consistent performance improvements can be achieved by using the regularization weight learned on per-image basis as opposed to a fixed regularization weight for all images in the dataset.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".