Name
Title | ||
|---|---|---|
Temperature network for few-shot learning with distribution-aware large-margin metric |
Abstract | ||
|---|---|---|
•A simple and general approach is proposed to enhance the prototype-based few-shot learning methods, which can theoretically lead to compact intra-class distributions.•We propose the Temperature Network which can implicitly generate query-specific prototypes. Moreover, in order to best utilize limited training samples, we further propose to train in a hard mode to exhaustively mine the large-margin metric.•We conduct comprehensive experiments on several publicly available datasets as well as the proposed Dermnet skin disease dataset to validate the proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107797 | Pattern Recognition |
Keywords | DocType | Volume |
Few-shot learning,Metric learning,Skin lesion classification,Temperature function | Journal | 112 |
Issue | ISSN | Citations |
1 | 0031-3203 | 2 |
PageRank | References | Authors |
0.37 | 23 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Zhu | 1 | 2 | 0.71 |
| Wenbin Li | 2 | 44 | 8.55 |
| Haofu Liao | 3 | 27 | 6.97 |
| Jiebo Luo | 4 | 6314 | 374.00 |