Eigenpaxels and a neural-network approach to image classification
Why this work is in the frame
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Bibliographic record
Abstract
A expansion encoding approach to image classification is presented. Localized principal components or "eigenpaxels" are used as a set of basis functions to represent images. That is, principal-component analysis is applied locally rather than on the entire image. The "eigenpaxels" are statistically determined using a database of the images of interest. Classification based on visual similarity is achieved through the use of a single-layer error-correcting neural network. Expansion encoding and the technique of subsampling are key elements in the processing stages of the eigenpaxel algorithm. Tested using a database of frontal face images consisting of 40 individuals, the algorithm exhibits equivalent performance to other comparable but more cumbersome methods. In addition, the technique is shown to be robust to various types of image noise.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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