A Pre-Trained Vs Fine-Tuning Methodology in Transfer Learning. Issue 1 (June 2021)

• Record Type: Journal Article
• Authors: Gupta, Neeraj
• Is Part Of: Journal of physics. Volume 1947:Issue 1(2021)
• Publication Date: 2021-06
• Subjects: ConvNet -- Convolutional Neural Network (CNN) -- Transfer learning; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1947/1/012028
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Transfer learning from a pre-trained and fine-tuning methodology has been utilized for the image classification. In this paper, we classify images of cats and dogs. It is much faster and easier than training from scratch using a pre-trained network with transfer learning. This saved network was trained previously on a huge dataset and known as a pre-trained model. The pre-trained model can be used in two ways, either it is used in the same way or it utilizes transfer learning approach to adapt this pre-trained network to a specific goal. Since the idea for image classification using transfer learning is that it is trained on the general and large dataset and further this model will be efficiently worked on the visual perception task of the real world. In fine-tuning, instead of tuning with the weights of generic feature map of based model, it is tuned accordingly to the dataset. In this paper, we take benefit of these learned feature maps deprived of having to start from scratch by utilizing a deep learning technique on a huge dataset and analyze the various methodology. The experimental results show that fine-tuning methodology outperformed over the pre-trained with feature extraction on accuracy.