Data Augmentation Methods for Low Resolution Facial 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
Regularization techniques are useful in alleviating the problem of overfitting in super-resolution using deep learning. In this paper, multiple kinds of augmentation techniques such as CutOut, CutMix, Cutblur, Blend and Mix of augmentation techniques (MOA) are applied to CELEBA database. Both MOA and CutBlur methods improve the performance of face super resolution method. The first set of experiments are conducted without applying augmentation methods. Next set of experiments are conducted applying both MOA augmentation and CutOut, CutMix, CutBlur, Blend methods. The experiments are carried out for different values of patch size and CutBlur ratio. CELEBA dataset is used for the experiment. The effectiveness of the application of data augmentation technique is tested in EDSR model. The results shows that data augmentation techniques improve the performance of super resolution methods. CutBlur technique performs the best among all the methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.005 | 0.002 |
| 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