Dual-Encoding Y-ResNet for generating a lens flare effect in images
Bibliographic record
Abstract
Taking photos against the light generates a certain visual effect. Taking a photo in the direction where the sun is located also results in a change in the temperature of the photo as well as the appearance of a visual effect in the form of the flare of light. In this article, we present an innovative neural network model called Y-ResNet, whose input consists of two samples and the output consists of one. This solution makes it possible to train the network by providing the original image and the flare effect, which will result in a modified sample. The training was conducted on a commonly known CityScapes dataset, where, by using classic data processing methods and the k-means algorithm, it was possible to add a flare if there was a visible portion of the sky in the input image. The proposed solution was described and tested to demonstrate the capabilities of the proposed method. The results show the superiority of the approach against the traditional ResNet without a second encoding path, generating better results, and creating a better impression of the lens-flare effect.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".