Name
Abstract | ||
|---|---|---|
In recent years, deep learning has been developed very quickly, and related research has shown a blossoming scene. Inception-v4 is a wide and deep network with good classification performance. The network structure is very complex, with convolution operations of different sizes, but there are two limitations: the inability to adaptively select the convolution kernel according to the characteristics of the image and the feature extraction from the high-level layer is not strong. This paper focuses on the investigation on the Inception-v4 model and has made several improvements. The improved Inception-v4 model is named BeIn-v4, which integrates the ideas of the Selective Kernel Network (SKNet) into the Inception-v4 network, and adjusts the network structure to achieve improvements. A number of comparative experiments have been carried out on the network before and after the improvements. The experimental results show that BeIn-v4 can obtain better classification results on the tested image datasets than Inception-v4. (C) 2022 Elsevier B.V. All rights reserved.& nbsp; |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.asoc.2022.108582 | APPLIED SOFT COMPUTING |
Keywords | DocType | Volume |
Deep learning, Inception-v4, Kernel, Selective, Classification | Journal | 119 |
ISSN | Citations | PageRank |
1568-4946 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Chen | 1 | 6 | 4.07 |
| Jiangshu Wei | 2 | 0 | 0.34 |
| Bing Xue | 3 | 21 | 13.38 |
| Mengjie Zhang | 4 | 3777 | 300.33 |