ACTIVE REINFORCEMENT LEARNING FOR THE SEMANTIC SEGMENTATION OF IMAGES CAPTURED BY MOBILE SENSORS
Bibliographic record
Abstract
Abstract. In recent years, various Convolutional Neural Networks (CNN) have been used to achieve acceptable performance on semantic segmentation tasks. However, these supervised learning methods require an extensive amount of annotated training data to perform well. Additionally, the model would need to be trained on the same kind of dataset to generalize well for other tasks. Further, commonly real world datasets are usually highly imbalanced. This problem leads to poor performance in the detection of underrepresented classes, which could be the most critical for some applications. The annotation task is time-consuming human labour that creates an obstacle to utilizing supervised learning methods on vision tasks. In this work, we experiment with implementing a reinforced active learning method with a weighted performance metric to reduce human labour while achieving competitive results. A deep Q-network (DQN) is used to find the optimal policy, which would be choosing the most informative regions of the image to be labelled from the unlabelled set. Then, the neural network would be trained with newly labelled data, and its performance would be evaluated. A weighted Intersection over Union (IoU) is used to calculate the rewards for the DQN network. By using weighted IoU, we target to bring more attention to underrepresented classes.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".