Few-Shot Learning UAV Recognition Methods Based on the Tri-Residual Semantic Network
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) recognition is of increasing importance since UAV is widely applied and imposes threats to the public safety. Although many UAV recognition methods based on deep learning have been proposed by using the radio frequency fingerprints, they depend on a large amount of training samples or have poor performance when the training samples are few. In this letter, in order to tackle those issues, two few-shot learning UAV recognition methods are proposed based on our designed tri-residual semantic network. Moreover, our proposed tri-residual semantic network not only can extract different levels of the feature information, but also can significantly suppress the effect of interference and noise. Simulation results demonstrate that our proposed methods are superior to the benchmark few-shot learning schemes in terms of the recognition accuracy.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it