# Regularization and Optimization strategies in Deep Convolutional Neural Network

arXiv: https://arxiv.org/abs/1712.04711 · HTML: https://mlsanity.com/paper/1712.04711/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2017.

## Built on by (most cited first, 3 of 3)

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- [Data-Augmented Deep Learning for Downhole Depth Sensing and Validation](https://mlsanity.com/paper/2511.00129/) (2025)
- [Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference](https://mlsanity.com/paper/2609.05275/) (2026)

## Abstract (arXiv)

Convolution Neural Networks, known as ConvNets exceptionally perform well in many complex machine learning tasks. The architecture of ConvNets demands the huge and rich amount of data and involves with a vast number of parameters that leads the learning takes to be computationally expensive, slow convergence towards the global minima, trap in local minima with poor predictions. In some cases, architecture overfits the data and make the architecture difficult to generalise for new samples that were not in the training set samples. To address these limitations, many regularization and optimization strategies are developed for the past few years. Also, studies suggested that these techniques significantly increase the performance of the networks as well as reducing the computational cost. In implementing these techniques, one must thoroughly understand the theoretical concept of how this technique works in increasing the expressive power of the networks. This article is intended to provide the theoretical concepts and mathematical formulation of the most commonly used strategies in developing a ConvNet architecture.