Why this work is in the frame
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Bibliographic record
Abstract
Visual perception is a challenging problem in part due to illumination variations. A pos-sible solution is to first estimate an illumi-nation invariant representation before using it for recognition. The object albedo and surface normals are examples of such rep-resentations. In this paper, we introduce a multilayer generative model where the latent variables include the albedo, surface normals, and the light source. Combining Deep Be-lief Nets with the Lambertian reflectance as-sumption, our model can learn good priors over the albedo from 2D images. Illumina-tion variations can be explained by changing only the lighting latent variable in our model. By transferring learned knowledge from sim-ilar objects, albedo and surface normals es-timation from a single image is possible in our model. Experiments demonstrate that our model is able to generalize as well as im-prove over standard baselines in one-shot face recognition. 1.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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