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Papers/Stochastic Optimization of Plain Convolutional Neural Netw...

Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods

Yahia Assiri

2020-01-24Image ClassificationData AugmentationStochastic Optimization
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Abstract

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.

Results

TaskDatasetMetricValueModel
Image ClassificationCIFAR-10Percentage correct94.29Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods
Image ClassificationCIFAR-100PARAMS4252298SOPCNN
Image ClassificationCIFAR-100Percentage correct72.96SOPCNN
Image ClassificationMNISTAccuracy99.83SOPCNN (Only a single Model)
Image ClassificationMNISTPercentage error0.17SOPCNN (Only a single Model)
Image ClassificationMNISTTrainable Parameters1400000SOPCNN (Only a single Model)
Image ClassificationSTL-10Percentage correct88.08SOPCNN
Image ClassificationSVHNPercentage error1.5SOPCNN

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