Improved Image Classification using Lightweight Deep Neural Network Enhancements
Bibliographic record
Abstract
In this article, a novel hierarchical deep neural network (DNN) is introduced that augments an input DNN to significantly enhance its image classification accuracy while reducing the inference time and the hardware overhead. The architecture comprises a hybrid framework that combines binary classifiers based on Convolutional Neural Networks (CNNs) with refined classifiers employing Vision Transformers (ViTs). A distinctive training approach is employed, where embedded models are designed and trained based on image distributions processed by binary classifiers, enhancing the system’s precision and efficiency. An algorithm determines the optimal inclusion of components within a cascading structure, enabling the construction, training, and deployment of specialized deep-learning networks. Additionally, two algorithms are introduced to optimize the architecture for multi-GPU systems. Extensive experimentation across multiple baseline DNNs, including both CNNs and ViTs, and diverse datasets demonstrates the versatility and superiority of our proposed structure over traditional methods, with particularly strong improvements observed on larger and more complex datasets such as ImageNet.
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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 | 0.000 |
| 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) | 0.003 | 0.002 |
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".