@inproceedings{9c66919dfaa249179d4ae4627530b0b4,
title = "Image Classification with Transfer Learning and FastAI",
abstract = "Today deep learning has provided us with endless possibilities for solving problems in many domains. Diagnosing diseases, speech recognition, image classification, and targeted advertising are a few of its applications. Starting this process from scratch requires using large amounts of labeled data and significant cloud processing usage. Transfer learning is a deep learning technique that solves this problem by making use of a model that is pre-trained for a certain task and using it on a different task of a related problem. Therefore, the goal of the project is to utilize transfer learning and achieve near-perfect results using a limited amount of data and computation power. To demonstrate, an image classifier using FastAI that detects three types of birds with up to 94% accuracy is implemented. This approach can be applied to solve tasks that are limited by labeled data and would gain by knowledge learned from a related task.",
keywords = "Deep learning, Image classification, Transfer learning",
author = "Ujwal Gullapalli and Lei Chen and Jinbo Xiong",
note = "Publisher Copyright: {\textcopyright} 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021 ; Conference date: 23-07-2021 Through 25-07-2021",
year = "2021",
doi = "10.1007/978-3-030-89814-4_59",
language = "English",
isbn = "9783030898137",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "796--806",
editor = "Jinbo Xiong and Shaoen Wu and Changgen Peng and Youliang Tian",
booktitle = "Mobile Multimedia Communications - 14th EAI International Conference, Mobimedia 2021, Proceedings",
address = "Germany",
}