ODMTCNet: An Interpretable Multiview Deep Neural Network Architecture for Feature Representation
Bibliographic record
Abstract
Recently, deep cascade architecture-based algorithms have attracted wide attention and have been applied to numerous application domains successfully. Nevertheless, the black-box structure of such algorithms has always been considered the Achilles' heel by the machine learning community. Moreover, due to its data-driven nature, the deep cascade architecture likely causes over-fitting problems when there is no sufficient data available. In order to solve these pressing issues, this work proposes a novel multiview deep neural network (DNN) model, namely, optimal discriminant multiview tensor convolutional network (ODMTCNet), which integrates statistics-guided optimization (SGO) principles with the DNN architecture. Specifically, a discriminant multiview tensor convolution strategy is proposed and integrated with a deep cascade architecture. Different from the traditional DNN models, the parameters of the convolutional layers in ODMTCNet are determined by solving SGO problems. Based on the SGO principles, the relation between the optimal performance and parameters (e.g., the number of convolutional filters) can be analytically predicted, with each layer generating justified knowledge representations. In addition, information quality (IQ) is adopted to further improve multiview feature representation. Because of its unique design, ODMTCNet is able to handle different types of features (e.g., raw, hand-crafted, prior knowledge-based, and DNN-generated features), forming a general platform for multiview feature representation. To validate the genericness and effectiveness of the ODMTCNet model, we conducted experiments on five datasets of different scales: The Olivetti Research Lab (ORL) database, the Facial Recognition Technology (FERET) database, the ETH-80 database, the Caltech 256 database, and the nanyang technological university (NTU) red green blue-depth (RGB+D) 120 database. Experimental results show the superiority of the presented solution over the state-of-the-art. Implementation codes will be made available in the final version.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".