@inproceedings{0b6380b06dcc42759497db35f705346b,
title = "Comparing Convolutional Neural Networks and Transformers in a Points-of-Interest Experiment",
abstract = "This paper addresses a research gap by providing a unique comparative analysis of the most prevalent Deep Learning (DL) models for image classification, specifically focusing on Points-of-Interest (POI) and discusses their differences. Convolutional Neural Network (CNN) based models are trained on a POI dataset and their accuracy levels are noted. The paper then proceeds to compare them with a recent model called ViT, which is based on the Transformers architecture, and has the potential to surpass current accuracy levels and bring further innovation in the field of Deep Learning. For this comparative study, a random sample from the Places365 dataset is utilized and is referred as the mini-places dataset in this paper.",
keywords = "Artificial intelligence, Convolution neural networks, Deep learning, Machine learning, Points-of-interest, Transformers",
author = "Paraskevas Messios and Ioanna Dionysiou and Harald Gjermundr{\o}d",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 3rd International Conference on Innovations in Computing Research, ICR 2024 ; Conference date: 12-08-2024 Through 14-08-2024",
year = "2024",
doi = "10.1007/978-3-031-65522-7_14",
language = "English",
isbn = "9783031655210",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "153--162",
editor = "Kevin Daimi and {Al Sadoon}, Abeer",
booktitle = "Proceedings of the 3rd International Conference on Innovations in Computing Research (ICR{\textquoteright}24)",
address = "Germany",
}