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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".