Name
Title | ||
|---|---|---|
Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications. |
Abstract | ||
|---|---|---|
The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network tr... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TPAMI.2017.2656884 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | Field | DocType |
Convolutional codes,Training,Feature extraction,Encoding,Knowledge engineering,Biological neural networks | Competitive learning,Multi-task learning,Semi-supervised learning,Pattern recognition,Inductive transfer,Computer science,Transfer of learning,Unsupervised learning,Knowledge engineering,Artificial intelligence,Deep learning,Machine learning | Journal |
Volume | Issue | ISSN |
40 | 5 | 0162-8828 |
Citations | PageRank | References |
12 | 0.52 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hang Chang | 1 | 374 | 29.11 |
| Ju Han | 2 | 145 | 8.74 |
| Cheng Zhong | 3 | 117 | 22.02 |
| Antoine Snijders | 4 | 12 | 0.52 |
| Jianhua Mao | 5 | 13 | 2.56 |