Visual scenes clustering using variational incremental learning of infinite generalized Dirichlet mixture models
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we develop a clustering approach based on variational incremental learning of a Dirichlet process of generalized Dirichlet (GD) distributions. Our approach is built on nonparametric Bayesian analysis where the determination of the complexity of the mixture model (i.e. the number of components) is sidestepped by assuming an infinite number of mixture components. By leveraging an incremental variational inference algorithm, the model complexity and all the involved model’s parameters are estimated simultaneously and effectively in a single optimization framework. Moreover, thanks to its incremental nature and Bayesian roots, the proposed framework allows to avoid over- and under-fitting problems, and to offer good generalization capabilities. The effectiveness of the proposed approach is tested on a challenging application involving visual scenes clustering. 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 | 0.000 | 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