# Convolutional Neural Networks and Vision Transformers for Fashion MNIST Classification: A Literature Review

arXiv: https://arxiv.org/abs/2406.03478 · HTML: https://mlsanity.com/paper/2406.03478/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 106 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 30 of 39)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [Going deeper with Image Transformers](https://mlsanity.com/paper/2103.17239/) (2021)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [CoAtNet: Marrying Convolution and Attention for All Data Sizes](https://mlsanity.com/paper/2106.04803/) (2021)
- [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://mlsanity.com/paper/2103.10697/) (2021)
- [DeiT III: Revenge of the ViT](https://mlsanity.com/paper/2204.07118/) (2022)
- [AdaViT: Adaptive Tokens for Efficient Vision Transformer](https://mlsanity.com/paper/2112.07658/) (2021)
- [Post-Training Quantization for Vision Transformer](https://mlsanity.com/paper/2106.14156/) (2021)
- [Conformer: Local Features Coupling Global Representations for Visual Recognition](https://mlsanity.com/paper/2105.03889/) (2021)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Rethinking Semantic Segmentation: A Prototype View](https://mlsanity.com/paper/2203.15102/) (2022)
- [Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference](https://mlsanity.com/paper/2204.07305/) (2022)
- [A Survey of Visual Transformers](https://mlsanity.com/paper/2111.06091/) (2021)
- [LambdaNetworks: Modeling Long-Range Interactions Without Attention](https://mlsanity.com/paper/2102.08602/) (2021)
- [Learning long-range spatial dependencies with horizontal gated-recurrent units](https://mlsanity.com/paper/1805.08315/) (2018)
- [Dilated Neighborhood Attention Transformer](https://mlsanity.com/paper/2209.15001/) (2022)
- [Squeeze-and-Attention Networks for Semantic Segmentation](https://mlsanity.com/paper/1909.03402/) (2019)
- [A Survey on Transformer Compression](https://mlsanity.com/paper/2402.05964/) (2024)
- [NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Images](https://mlsanity.com/paper/2303.12012/) (2023)
- [Analyzing Vision Transformers for Image Classification in Class Embedding Space](https://mlsanity.com/paper/2310.18969/) (2023)

## Built on by (most cited first, 2 of 2)

- [Large Language Models Implicitly Learn to See and Hear Just By Reading](https://mlsanity.com/paper/2505.17091/) (2025)
- [Beyond Accuracy: Reliability and Uncertainty Estimation in Convolutional Neural Networks](https://mlsanity.com/paper/2603.10731/) (2026)

## Abstract (arXiv)

Our review explores the comparative analysis between Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in the domain of image classification, with a particular focus on clothing classification within the e-commerce sector. Utilizing the Fashion MNIST dataset, we delve into the unique attributes of CNNs and ViTs. While CNNs have long been the cornerstone of image classification, ViTs introduce an innovative self-attention mechanism enabling nuanced weighting of different input data components. Historically, transformers have primarily been associated with Natural Language Processing (NLP) tasks. Through a comprehensive examination of existing literature, our aim is to unveil the distinctions between ViTs and CNNs in the context of image classification. Our analysis meticulously scrutinizes state-of-the-art methodologies employing both architectures, striving to identify the factors influencing their performance. These factors encompass dataset characteristics, image dimensions, the number of target classes, hardware infrastructure, and the specific architectures along with their respective top results. Our key goal is to determine the most appropriate architecture between ViT and CNN for classifying images in the Fashion MNIST dataset within the e-commerce industry, while taking into account specific conditions and needs. We highlight the importance of combining these two architectures with different forms to enhance overall performance. By uniting these architectures, we can take advantage of their unique strengths, which may lead to more precise and reliable models for e-commerce applications. CNNs are skilled at recognizing local patterns, while ViTs are effective at grasping overall context, making their combination a promising strategy for boosting image classification performance.