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Evaluating Transfer Learning Capabilities of Neural NetworkArchitectures for Image Classification

Training a deep neural network from scratch can be very expensive in terms of resources.In addition, training a neural network on a new task is usually done by training themodel form scratch. Recently there are new approaches in machine learning which usesthe knowledge from a pre-trained deep neural network on a new task. The technique ofreusing the knowledge from previously trained deep neural networks is called Transferlearning. In this paper we are going to evaluate transfer learning capabilities of deep neuralnetwork architectures for image classification. This research attempts to implementtransfer learning with different datasets and models in order to investigate transfer learningin different situations.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:oru-100034
Date January 2022
CreatorsDarouich, Mohammed, Youmortaji, Anton
PublisherÖrebro universitet, Institutionen för naturvetenskap och teknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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