AlphaMEX: A smarter global pooling method for convolutional neural networks
Bibliographic record
Abstract
Deep convolutional neural networks have achieved great success on image classification. A series of feature extractors learned from CNN have been used in many computer vision tasks. Global pooling layer plays a very important role in deep convolutional neural networks. It is found that the input feature-maps of global pooling become sparse, as the increasing use of Batch Normalization and ReLU layer combination, which makes the original global pooling low efficiency. In this paper, we proposed a novel end-to-end trainable global pooling operator AlphaMEX Global Pool for convolutional neural network. A nonlinear smooth log-mean-exp function is designed, called AlphaMEX, to extract features effectively and make networks smarter. Compared to the original global pooling layer, our proposed method can improve classification accuracy without increasing any layers or too much redundant parameters. Experimental results on CIFAR-10/CIFAR100, SVHN and ImageNet demonstrate the effectiveness of the proposed method. The AlphaMEX-ResNet outperforms original ResNet-110 by 8.3% on CIFAR10+, and the top-1 error rate of AlphaMEX-DenseNet (k = 12) reaches 5.03% which outperforms original DenseNet (k = 12) by 4.0%.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".