Ship Detection for Satellite Images based on Classifier Transfer Learning Combined with Feature Transfer Learning
Bibliographic record
Abstract
Transfer learning (TL) is a powerful tool to transfer deep learning models from a large source dataset to a small target dataset, but the upper-layers of deep learning models are less transferable for lacking universality and possessing specificity to certain tasks.Most researches have focused on feature-oriented transfer learning base on the feature space, however, both the classifieroriented transfer learning and the label space haven't been considered.Faced with these issues, a generalized classifier-oriented transfer learning, termed as classifier-TL, is proposed in this paper, which investigates the correlation between source label space and target label space to transfer and refine the generalized classifier.More specifically, for a given task, a label space descriptor is proposed to depict the label space, and a label space similarity is introduced to measure the correlation between source label space and target label space.Then, the target label space is focused through the proposed label driven posteriori optimization, trying to exploit similar label spaces of the closest category.In this procedure, the classifier can be refined from a set of generalized classifiers to a specific classifier.Furthermore, this classifier-TL can be combined with the traditional feature-oriented transfer learning, to form an integrative secondary transfer learning, for further boosting the performance of transfer learning.Experimental results for the task of ship detection, have demonstrated the effectiveness of our proposed method.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".