Eye illusion enhancement using interactive Differential Evolution
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
Eye illusion is one of the most interesting topics which attracts majority of the people. In this paper, an interactive evolutionary technique has been proposed to improve the illusion factor in eye illusion images. Eye illusion or vision illusion is subjective and can differ from person to person. This technique utilizes an evolutionary algorithm, namely Differential Evolution (DE), to improve the vision deceiving factor for an Eye Illusion image. Modeling of human vision perception is impossible or at least very complicated even for a specific person. So, proposing a general fitness function as an optimization objective would not be an easy task. That is why an interactive optimization approach seems a reasonable approach in this regard. To the best of our knowledge, the current work is the first attempt which utilizes an interactive optimization technique to enhance vision illusion images. Performance of the proposed approach is verified on two eye illusion test cases, but that is applicable to other eye illusion image enhancements and also image processing tasks, such as image filtering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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