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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 ...
Ahmet Arslan, Baris Kocer
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Adaptive transfer learning [PDF]
The Annals of Statistics, 2021 In transfer learning, we wish to make inference about a target population when we have access to data both from the distribution itself, and from a different but related source distribution. We introduce a flexible framework for transfer learning in the context of binary classification, allowing for covariate-dependent relationships between the source ...
Henry W. J. Reeve +2 more
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Journal of Ethnic Foods, 2022 The increased frequency of extreme climate-induced natural disasters (floods, cyclones, mud slides, heat waves, droughts), attributed to climate change, is causing stress to already vulnerable livelihoods by affecting both tangible and intangible ...
Vimbainashe Prisca Dembedza +3 more
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Energies, 2021 The intermittent nature of renewable sources, such as solar and wind, leads to the need for a hybrid renewable energy system (HRES) that can provide uninterrupted and reliable energy to a remote and off-grid location with the use of a biogas generator ...
Vijay Mudgal +6 more
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Learning to Learn Transferable Attack
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. However, the transferability of perturbations from existing methods is still limited, since the adversarial perturbations are easily overfitting with a single
Shuman Fang +3 more
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Transfer Learning and Curriculum Learning in Sokoban [PDF]
, 2022 Transfer learning can speed up training in machine learning and is regularly used in classification tasks. It reuses prior knowledge from other tasks to pre-train networks for new tasks. In reinforcement learning, learning actions for a behavior policy that can be applied to new environments is still a challenge, especially for tasks that involve much ...
Zhao Yang 0003, Mike Preuss, Aske Plaat
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Health Science Reports, 2022 Background and Aims In low‐income countries where there is shortage of appropriate medical care to manage hypertension (HTN), understanding dynamics of communities' knowledge and attitude to prevent through lifestyle is crucial.
Tsegab Paulose +2 more
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Adaptive Transfer Learning: a simple but effective transfer learning
CoRR, 2021 Transfer learning (TL) leverages previously obtained knowledge to learn new tasks efficiently and has been used to train deep learning (DL) models with limited amount of data. When TL is applied to DL, pretrained (teacher) models are fine-tuned to build domain specific (student) models.
Jung H. Lee +9 more
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When & How to Transfer with Transfer Learning
CoRR, 2022 In deep learning, transfer learning (TL) has become the de facto approach when dealing with image related tasks. Visual features learnt for one task have been shown to be reusable for other tasks, improving performance significantly. By reusing deep representations, TL enables the use of deep models in domains with limited data availability, limited ...
Adrian Tormos +3 more
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Progressive Transfer Learning [PDF]
IEEE Transactions on Image Processing, 2022 10 pages, 4 figures, journel verison of our published short paper on ...
Zhengxu Yu +5 more
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