Learning a Discriminative Grassmannian Neural Network for Visual Classification
Bibliographic record
Abstract
Learning representations on the Grassmannian manifold is popular in quite a few visual classification tasks. With the development of deep learning techniques, several neural networks have recently emerged for processing subspace data. However, the diversely changed appearance of the signal data (video clips and image sets), makes it impossible for the existing Grassmannian networks (GrasNets) that rely on a single cross-entropy loss for end-to-end training to learn effective geometric representations, especially for complicated visual scenarios. To solve this problem, a Riemannian triplet loss-based Riemannian metric learning mechanism is introduced to the original GrasNet, which can explicitly encode and learn the characteristics of the intra- and inter-class data distributions conveyed by the input data during network training. Additionally, given the existence of intra-class diversity and inter-class ambiguity of the input data, we propose a hard sample reward strategy (HSR) to further improve the discriminability of the learned network embedding. Extensive experimental results obtained on four benchmarking datasets demonstrate the effectiveness of the proposed method.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".