Papers › Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods
Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods
Yahia Assiri
Convolutional neural networks have been achieving the best possible accuracies in many visual pattern classification problems. However, due to the model capacity required to capture such representations, they are often oversensitive to overfitting and therefore require proper regularization to generalize well. In this paper, we present a combination of regularization techniques which work together to get better performance, we built plain CNNs, and then we used data augmentation, dropout and customized early stopping function, we tested and evaluated these techniques by applying models on five famous datasets, MNIST, CIFAR10, CIFAR100, SVHN, STL10, and we achieved three state-of-the-art-of (MNIST, SVHN, STL10) and very high-Accuracy on the other two datasets.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods | Percentage correct | 94.29 | #160 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | SOPCNN | PARAMS | 4,252,298 | #164 of 211 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | SOPCNN | Percentage correct | 72.96 | #164 of 211 | Archive leaderboard | report |
| Image Classification | MNIST | SOPCNN (Only a single Model) | Accuracy | 99.83 | #4 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | SOPCNN (Only a single Model) | Percentage error | 0.17 | #4 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | SOPCNN (Only a single Model) | Trainable Parameters | 1400000 | #4 of 81 | Archive leaderboard | report |
| Image Classification | STL-10 | SOPCNN | Percentage correct | 88.08 | #43 of 117 | Archive leaderboard | report |
| Image Classification | SVHN | SOPCNN | Percentage error | 1.50 | #10 of 62 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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