This repo is a collection of AWESOME papers, code related with transfer learning, pre-training and domain adaptation etc. Feel free to star and fork. Feel free to let us know the missing papers (issue ...

Furthermore, domain adaptation (DA) has been the most common TL method in general, whereas inductive transfer learning (ITL) has been rare. To the best of our knowledge, DA and ITL have never been ...

@Misc{transferlearning.xyz, howpublished = {\url{http://transferlearning.xyz}}, title = {Everything about Transfer Learning and Domain Adapation}, author = {Wang ...

Transfer learning is a technique that allows you to reuse the knowledge gained from a source domain or task to a target domain or task. For example, you can use a pre-trained model that was ...

To address the first question, we apply Shapley values to investigate how and why transfer learning improves model accuracy, and also propose and test a domain adaptation approach to address the ...

To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: ...

In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are ...