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Transfer learning for medical image classification: a literature review
BMC Medical Imaging, 2022 Background Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance.
Hee E. Kim +5 more
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Neural Transfer Learning for Repairing Security Vulnerabilities in C Code [PDF]
IEEE Transactions on Software Engineering, 2021 In this paper, we address the problem of automatic repair of software vulnerabilities with deep learning. The major problem with data-driven vulnerability repair is that the few existing datasets of known confirmed vulnerabilities consist of only a few ...
Zimin Chen +2 more
semanticscholar +1 more source
Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique
Sustainability, 2023 Due to the rapid emergence and evolution of AI applications, the utilization of smart imaging devices has increased significantly. Researchers have started using deep learning models, such as CNN, for image classification.
Yonis Gulzar
semanticscholar +1 more source
Electroencephalogram-Based Preference Prediction Using Deep Transfer Learning
IEEE Access, 2020 Transfer learning is an approach in machine learning where a model that was built and trained on one task is re-purposed on a second task. The success of transfer learning in computer vision has motivated its use in neuroscience. Although common in image
Mashael S. Aldayel +2 more
doaj +1 more source
Taskonomy: Disentangling Task Transfer Learning [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks.
Amir Zamir +5 more
semanticscholar +1 more source
Transfer learning: a friendly introduction
Journal of Big Data, 2022 Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research ...
Asmaul Hosna +5 more
semanticscholar +1 more source
Frontiers in Psychology, 2018 Introduction: Consolidation is defined as the time necessary for memory stabilization after learning. In the present study we focused on effects of interference during the first 12 consolidation minutes after learning.
Zrinka Sosic-Vasic +7 more
doaj +1 more source
Factors influencing the learning transfer of nursing students in a non-face-to-face educational environment during the COVID-19 pandemic in Korea: a cross-sectional study using structural equation modeling [PDF]
Journal of Educational Evaluation for Health Professions, 2023 Purpose The aim of this study was to identify factors influencing the learning transfer of nursing students in a non-face-to-face educational environment through structural equation modeling and suggest ways to improve the transfer of learning.
Geun Myun Kim +2 more
doaj +1 more source
Information-theoretic analysis for transfer learning [PDF]
, 2020 Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different distributions (denoted as $\mu$ and $\mu'$, respectively). In this work, we give an information-theoretic
Aickelin, Uwe +3 more
core +2 more sources
Expert Systems with Applications, 2010 Transfer learning is a method which aims to improve ''related'' tasks performance. Transfer learning tries to use information gained from related tasks solutions to improve performance of learning strategy. Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a ...
Koçer, Barış, Arslan, Ahmet
openaire +2 more sources