# Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods

arXiv: https://arxiv.org/abs/2001.08856 · HTML: https://mlsanity.com/paper/2001.08856/

## Key facts

- Cited by 7 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 24 Jan 2020.

## Built on by (most cited first, 7 of 7)

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- [Signature-Graph Networks](https://mlsanity.com/paper/2110.11551/) (2021)
- [CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis](https://mlsanity.com/paper/2209.09556/) (2022)
- [GoogLe2Net: Going Transverse with Convolutions](https://mlsanity.com/paper/2301.00424/) (2023)
- [AI-interpreted Optical Scattering for Robust and Focal Depth-Aware Imaging](https://mlsanity.com/paper/2607.22867/) (2026)

## Abstract (arXiv)

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.