A New Global Pooling Method for Deep Neural Networks: Global Average of Top-K Max-Pooling
Bibliographic record
Abstract
Global Pooling (GP) is one of the important layers in deep neural networks.GP significantly reduces the number of model parameters by summarizing the feature maps and enables a reduction in the computational cost of training.The most commonly used GP methods are global max pooling (GMP) and global average pooling (GAP).The GMP method produces successful results in experimental studies but has a tendency to overfit training data and may not generalize well to test data.On the other hand, the GAP method takes into account all activations in the pooling region, which reduces the effect of high activation areas and causes a decrease in model performance.In this study, a GP method called global average of top-k max pooling (GAMP) is proposed, which returns the average of the highest k activations in the feature map and allows for mixing the two methods mentioned.The proposed method is compared quantitatively with other GP methods using different models, i.e., Custom and VGG16-based and different datasets, i.e., CIFAR10 and CIFAR100.The experimental results show that the proposed GAMP method provides better image classification accuracy than the other GP methods.When the Custom model is used, the proposed GAMP method provides a classification accuracy of 1.29% higher on the CIFAR10 dataset and 1.72% higher on the CIFAR100 dataset compared to the method with the closest performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".