Transfer-Gan: Multimodal Ct Image Super-Resolution Via Transfer Generative Adversarial Networks
- Award ID(s):
- 1908299
- PAR ID:
- 10189696
- Date Published:
- Journal Name:
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
- Page Range / eLocation ID:
- 195 to 198
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Graph embeddings have been tremendously successful at producing node representations that are discriminative for downstream tasks. In this paper, we study the problem of graph transfer learning: given two graphs and labels in the nodes of the first graph, we wish to predict the labels on the second graph. We propose a tractable, noncombinatorial method for solving the graph transfer learning problem by combining classification and embedding losses with a continuous, convex penalty motivated by tractable graph distances. We demonstrate that our method successfully predicts labels across graphs with almost perfect accuracy; in the same scenarios, training embeddings through standard methods leads to predictions that are no better than random.more » « less
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