Name
Title | ||
|---|---|---|
Toward Fusing Domain Knowledge with Generative Adversarial Networks to Improve Supervised Learning for Medical Diagnoses |
Abstract | ||
|---|---|---|
This paper addresses the challenges of small training data in deep learning. We share our experiences in the medical domain and present promises and limitations. In particular, we show through experimental results that GANs are ineffective in generating quality training data to improve supervised learning. We suggest plausible research directions to remedy the problems. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MIPR.2019.00022 | 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) |
Keywords | Field | DocType |
Deep learning, knowledge-adaptive GANs, generative adversarial networks, transfer learning | Training set,Domain knowledge,Computer science,Transfer of learning,Supervised learning,Artificial intelligence,Deep learning,Generative grammar,Medical diagnosis,Machine learning,Adversarial system | Conference |
ISBN | Citations | PageRank |
978-1-7281-1198-8 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fu-Chieh Chang | 1 | 0 | 0.34 |
| Jocelyn J. Chang | 2 | 0 | 0.34 |
| Chun-Nan Chou | 3 | 3 | 4.09 |
| Edward Y. Chang | 4 | 4519 | 336.59 |