Optimization Method of Residual Networks of Residual Networks for Image Classification
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Bibliographic record
Abstract
The activation of a Deep Convolutional Neural Network that overlooks the diversity of datasets has been restricting its development in image classification. In this paper, we propose a Residual Networks of Residual Networks(RoR) optimization method. Firstly, three activation functions (ReLU, ELU and PELU) are applied to RoR and can provide more effective optimization methods for different datasets; Secondly, we added a drop-path to avoid over-fitting and widened RoR adding filters to avoid gradient vanish. Our networks achieved good classification accuracy in CIFAR-10/100 datasets, and the best test errors were 3.52 % and 19.07% on CIFAR-10/100, respectively. The experiments prove that the RoR network optimization method can improve network performance, and effectively restrain the vanishing/exploding gradients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it