Name
Abstract | ||
|---|---|---|
Variational autoencoders (VAEs) have demonstrated their superiority in unsupervised learning for image processing in recent years. The performance of the VAEs highly depends on their architectures, which are often handcrafted by the human expertise in deep neural networks (DNNs). However, such expertise is not necessarily available to each of the end users interested. In this article, we propose a... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TEVC.2020.3047220 | IEEE Transactions on Evolutionary Computation |
Keywords | DocType | Volume |
Computer architecture,Encoding,Task analysis,Approximation algorithms,Training,Genetic algorithms,Computer science | Journal | 25 |
Issue | ISSN | Citations |
5 | 1089-778X | 2 |
PageRank | References | Authors |
0.37 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangru Chen | 1 | 7 | 1.89 |
| yanan sun | 2 | 190 | 9.33 |
| Mengjie Zhang | 3 | 3777 | 300.33 |
| Dezhong Peng | 4 | 285 | 27.92 |