Optimization of deep learning architecture based on multi-path convolutional neural network algorithm
Bibliographic record
Abstract
Current multi-stream convolutional neural network (MSCNN) exhibits notable limitations in path cooperation, feature fusion, and resource utilization when handling complex tasks. To enhance MSCNN's feature extraction ability, computational efficiency, and model robustness, this study conducts an in-depth investigation of these architectural deficiencies and proposes corresponding improvements. At present, there are some problems in multi-path architecture, such as isolated information among paths, low efficiency of feature fusion mechanism, and high computational complexity. These issues lead to insufficient performance of the model in robustness indicators such as noise resistance, occlusion sensitivity, and resistance to sample attacks. The architecture also faces challenges in data scalability efficiency and resource scalability requirements. Therefore, this study proposes an optimized model based on a dynamic path cooperation mechanism and lightweight design, innovatively introducing a path attention mechanism and feature-sharing module to enhance information interaction between paths. Self-attention fusion method is adopted to improve the efficiency of feature fusion. At the same time, by combining path selection and model pruning technology, the effective balance between model performance and computational resources demand is realized. The study employs three datasets, Canadian Institute for Advanced Research-10 (CIFAR-10), ImageNet, and Custom Dataset for performance comparison and simulation. The results show that the proposed optimized model is superior to the current mainstream model in many indicators. For example, on the Medical Images dataset, the optimized model's noise robustness, occlusion sensitivity, and sample attack resistance are 0.931, 0.950, and 0.709, respectively. On E-commerce Data, the optimized model's data scalability efficiency reaches 0.969, and the resource scalability requirement is only 0.735, showing excellent task adaptability and resource utilization efficiency. Therefore, the study provides a critical reference for the optimization and practical application of MSCNN, contributing to the application research of deep learning in complex tasks.
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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 | 0.000 |
| 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) | 0.002 | 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 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".