primaryClass={cs.LG} } @misc{tllib, author = {Junguang Jiang, Baixu Chen, Bo Fu, Mingsheng Long}, title = {Transfer-Learning-library}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/thuml/Transfer-Learning-Library}}, ...
@misc{dalib, author = {Junguang Jiang, Bo Fu, Mingsheng Long}, title = {Transfer-Learning-library}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/thuml/Transfer-Learning-Library}}, } Acknowledgment We would like to thank...
https://github.com/thuml/Transfer-Learning-Library Kaya M, Hajimirza S (2019) Using bayesian optimization with knowledge transfer for high computational cost design: a case study in photovoltaics. In: International design engineering technical conferences and computers and information in engineering ...
Summary The participant must be committed to the change in order for learning to transfer. In addition, the supervisor must provide the learner with support. The trainer must support the learner while in the session as well as follow up after the session. To ensure that what was learned is...
The general model of the Channel-Boosted Transfer Learning-Based CNN (CBTL-CNN) is shown in Figure 2. Figure 2 Open in figure viewerPowerPoint General model of the Channel-Boosted Transfer Learning-Based CNN (CBTL-CNN). There are L numbers of auxiliary learners used in this model to ...
3.1 Preliminary: Transfer learning (TL) Transfer learning is proposed to solve the issue of data-unavailability [19, 20]. For example, the present masked-load data may not have sufficient learning samples owing to the dynamic DERs. In this case, the learning model individually dependent with pr...
The comparison between traditional machine learning process and transfer learning process is shown in Figure 3. Figure 3 Open in figure viewerPowerPoint Comparison the process of traditional machine and transfer learning. Firstly, the definition of transfer learning is analyzed. Given a source domain ...
Therefore, a deep learning training network for disease classification and recognition of multimodal few-shot medical images are proposed, trying to solve the above problems and limitations. The network is based on the idea of meta-learning for training. Specifically, the technology of transfer ...
Recent years have witnessed an increasing interest in transfer learning. This paper deals with the classification problem that the target-domain with a different distribution from the source-domain is totally unlabeled, and aims to build an inductive model for unseen data. Firstly, we analyze the ...
However, given that there is a potential physical relationship between sunspots and the magnetic field, we employ another machine learning technique called transfer learning which has recently received considerable attention in the literature. Here, this approach consists in first training the source ...