# Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification

arXiv: https://arxiv.org/abs/2204.05983 · HTML: https://mlsanity.com/paper/2204.05983/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 67 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [Mish: A Self Regularized Non-Monotonic Activation Function](https://mlsanity.com/paper/1908.08681/) (2019)
- [How Useful is Self-Supervised Pretraining for Visual Tasks?](https://mlsanity.com/paper/2003.14323/) (2020)
- [Fractional Max-Pooling](https://mlsanity.com/paper/1412.6071/) (2014)
- [Can Vision Transformers Perform Convolution?](https://mlsanity.com/paper/2111.01353/) (2021)
- [Deep Discriminative Representation Learning with Attention Map for Scene Classification](https://mlsanity.com/paper/1902.07967/) (2019)
- [Doubly Convolutional Neural Networks](https://mlsanity.com/paper/1610.09716/) (2016)

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

- [Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2](https://mlsanity.com/paper/2405.11621/) (2024)
- [A Novel Method for Accurate & Real-time Food Classification: The Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data Augmentation](https://mlsanity.com/paper/2410.02304/) (2024)
- [Deepfakes and Synthetic Media: Generation, Detection, and Governance](https://mlsanity.com/paper/2609.25017/) (2026)

## Abstract (arXiv)

The purpose of the study is to analyse and compare the most common machine learning and deep learning techniques used for computer vision 2D object classification tasks. Firstly, we will present the theoretical background of the Bag of Visual words model and Deep Convolutional Neural Networks (DCNN). Secondly, we will implement a Bag of Visual Words model, the VGG16 CNN Architecture. Thirdly, we will present our custom and novice DCNN in which we test the aforementioned implementations on a modified version of the Belgium Traffic Sign dataset. Our results showcase the effects of hyperparameters on traditional machine learning and the advantage in terms of accuracy of DCNNs compared to classical machine learning methods. As our tests indicate, our proposed solution can achieve similar - and in some cases better - results than existing DCNNs architectures. Finally, the technical merit of this article lies in the presented computationally simpler DCNN architecture, which we believe can pave the way towards using more efficient architectures for basic tasks.