Deep Tree Net-Vector of Locally Aggregated Descriptor (VLAD) Model
Bibliographic record
Abstract
In this work, we combined both a tree and a NetVLAD (vector of locally aggregated descriptors) in order to design new deep model. In developing this model, we studied the impact of tree hyperparameters and found that branch factors had major effects on the parameter utilization as major indicator and the accuracy of the model. The new architecture presented herein exposes a novel hyperparameter called the tree branch factor, which grants additional control over model complexity and on the maps codependency. Deep Tree Net-Vector provide the flexibility to combine two very famous techniques, namely the tree-based technique and VLAD. The former reduces the number of parameters, whereas the latter provides better feature representation inside the model. This work aimed to demonstrate the integration of the strong image descriptor, VLAD, with the tree module, to gain additional control on model size, rather than obtaining better results than state-of-the-art models, and to enhance image representation inside model layers, which could then be invested towards several tasks such as image classification and retrieval. We performed experiments on the Canadian Institute for Advanced Research-10 dataset and were able to show that the proposed models were superior-in terms of information density and accuracy-to many well-known networks.
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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 | 0.001 |
| 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 | 0.000 |
| 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".