Transfer Learning Based Super Resolution of Aerial Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Images created using the Super Resolution method can generate more information compared to their low resolution counterparts. A super-resolved image, which is created using an original image captured by an imaging source is not only more meaningful to human perception but also has advantages on downstream tasks such as object detection and pattern recognition. In this work, we aim to apply the Super Resolution method to the Aerial Images captured for surveillance to enable more information about the original scenes. To achieve this Super Resolution Generative Adversarial Network (SRGAN), which is based on the Generative Adversarial Networks architecture is used. We also applied transfer learning methodology to achieve better image quality. Public xView and DOTA datasets which contain images mostly captured by satellites around the world are used to train a generative model via SRGAN architecture. Furthermore, DIV2K dataset is used to pre-train a generative model, and then the transfer learning technique is used to train separate models on xView and DOTA validation datasets. Perceptual Index (PI) and Root Mean Squared Error (RMSE) which are used on European Conference on Computer Vision -Perceptual Image Restoration and Manipulation Workshop 2018 are computed as the performance metrics. We have seen that the model which gives the best PI results, i.e. better perceptual quality, on xView and DOTA validation datasets is the one trained using the DIV2K dataset and the model which gives the best RMSE results, i.e. better reconstruction quality, is the one trained using the transfer learning technique.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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