| Popis: |
In this paper, traditional one-hot encoded network is compared with three deep metric learning methods by using contrastive loss, triplet loss, and quadruplet loss on character classification task. Experimental results show that deep metric learning methods provide similar performance to one-hot encoded network, but have more advantages and less label requirements. On the other hand, by comparing these well-known deep metric learning methods under the same number of parameters, their differences are also summarized. |
