Consecutive Dimensionality Reduction by Canonical Correlation Analysis for Visualization of Convolutional Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we develop a visualization tool suitable for deep neural networks (DNN). Although typical dimensionality reduction methods, such as principal component analysis (PCA), are useful to visualize highdimensional data as 2 or 3 dimensional representations, most of those methods focus their attention on how to create essential subspaces based only on a given unique feature representation. On the other hand, DNN naturally have consecutive multiple feature representations corresponding to their intermediate layers. In order to understand relationships of those consecutive intermediate layers, we utilize canonical correlation analysis (CCA) to visualize them in a unified subspace. Our method (called consecutive CCA) can visualize “feature flow” which represents movement of samples between two consecutive layers of DNN. By using standard benchmark datasets, we show that our visualization results contain much information that typical visualization methods (such as PCA) do not represent.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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