Name
Abstract | ||
|---|---|---|
Learning from synthetic data has many important applications in case where sufficient amounts of labeled data are not available. Using synthetic data is challenging due to differences in feature distributions between synthetic and actual data, a phenomenon we term synthetic gap. In this paper, we investigate and formalize a general framework – Stacked Multichannel Autoencoder (SMCAE) that enables bridging the synthetic gap and learning from synthetic data more efficiently. In particular, we show that our SMCAE can not only transform and use synthetic data on a challenging face-sketch recognition task, but that it can also help simulate real images which can be used for training classifiers for recognition. Preliminary experiments validate the effectiveness of the proposed framework. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-018-5879-7 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Multimodal autoencoder, Synthetic gap, Satellite image classification, Learning from synthetic data, Face-sketch recognition | Satellite image classification,Autoencoder,Pattern recognition,Computer science,Bridging (networking),Synthetic data,Artificial intelligence,Real image,Labeled data | Journal |
Volume | Issue | ISSN |
77 | 20 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Zhang | 1 | 40 | 28.57 |
| Yanwei Fu | 2 | 543 | 51.93 |
| Shanshan Jiang | 3 | 129 | 20.15 |
| Xiangyang Xue | 4 | 2466 | 154.25 |
| Yu-Gang Jiang | 5 | 3071 | 152.58 |
| Gady Agam | 6 | 391 | 43.99 |