Results 11 to 20 of about 295,561 (260)
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning [PDF]
CoRR, 2020 We propose a novel adaptive transfer learning framework, learning to transfer learn (L2TL), to improve performance on a target dataset by careful extraction of the related information from a source dataset. Our framework considers cooperative optimization of shared weights between models for source and target tasks, and adjusts the constituent loss ...
Linchao Zhu +3 more
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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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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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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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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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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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Inteligencia Artificial, 2022 Deep Learning and transfer learning models are being used to generate time series forecasts; however, there is scarce evidence about their performance prediction that it is more evident for monthly time series.
Martín Solís +1 more
doaj +3 more sources
Quantum deep transfer learning
New Journal of Physics, 2021 Quantum machine learning (QML) has aroused great interest because it has the potential to speed up the established classical machine learning processes.
Longhan Wang, Yifan Sun, Xiangdong Zhang
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