Name
Abstract | ||
|---|---|---|
Generalized zero-shot learning is a significant topic but faced with bias problem, which leads to unseen classes being easily misclassified into seen classes. Hence we propose a embedding model called co-representation network to learn a more uniform visual embedding space that effectively alleviates the bias problem and helps with classification. We mathematically analyze our model and find it learns a projection with high local linearity, which is proved to cause less bias problem. The network consists of a cooperation module for representation and a relation module for classification, it is simple in structure and can be easily trained in an end-to-end manner. Experiments show that our method outperforms existing generalized zero-shot learning methods on several benchmark datasets. |
Year | Venue | Field |
|---|---|---|
2019 | international conference on machine learning | Pattern recognition,Computer science,Zero shot learning,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| zhang fei | 1 | 24 | 7.85 |
| Guangming Shi | 2 | 2663 | 184.81 |