Multiview Transfer Learning and Multitask Learning

Sun, Shiliang; Mao, Liang; Dong, Ziang; Wu, Lidan

doi:10.1007/978-981-13-3029-2_7

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[70] is a book chapter which discusses types of transfer learning and describes MTL as a variant of transfer learning. While [71], [72], details types of multi-view transfer learning and multi-view MTL. The articles [73], [74] gives theoretical concepts of improving the performance of MTL and transfer learning in general.…”

Section: B Research In Multi-task Transfer Learningmentioning

confidence: 99%

Sharing to learn and learning to share -- Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review

Upadhyay¹,

Phlypo²,

Saini³

et al. 2021

Preprint

View full text Add to dashboard Cite

Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms like transfer learning, meta learning, and multi-task learning reflect the human learning process by exploiting the prior knowledge for new tasks, encouraging faster learning and good generalization for new tasks. This article gives a detailed view of these learning paradigms along with a comparative analysis. The weakness of a learning algorithm turns out to be the strength of another, and thereby merging them is a prevalent trait in the literature. This work delivers a literature review of the articles, which fuses two algorithms to accomplish multiple tasks. A global generic learning network, an ensemble of meta learning, transfer learning, and multi-task learning, is also introduced here, along with some open research questions and directions for future research.

show abstract

Section: B Research In Multi-task Transfer Learningmentioning

confidence: 99%

Sharing to learn and learning to share -- Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review

Upadhyay¹,

Phlypo²,

Saini³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Alternatively, semi-supervised learning [1491][1492][1493][1494][1495][1496] can generate target outputs for a dataset of unpaired inputs to augment training with a dataset of paired inputs and target outputs. Finally, multitask learning [1497][1498][1499][1500][1501] can improve performance by introducing additional loss functions. For instance, by adding an auxiliary classifier to predict image labels from features generated by intermediate DNN layers [1502][1503][1504][1505] .…”

Section: Regularizationmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

View full text Add to dashboard Cite

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

show abstract