Triplet Loss首次在FaceNet: A Unified Embedding for Face Recognition and Clustering这篇论文中被提出,目的是使网络学到更好的人脸表征,即同一个人的不同输入通过网络输出的 表征间距离尽量小,不同人得到的 表征距离尽量大。 不同于Contrastive Loss,Triplet Loss构造了一个三元组计算损失。<a,p,n>分别表示ancho...
. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss the center loss simultaneously le...
The proposed contrastive-center loss simultaneously considers intra-class compactness and inter-class separability, by penalizing the contrastive values between: (1)the distances of training samples to their corresponding class centers, and (2)the sum of the distances of training samples to their non-...
Furthermore, SACT introduces a new loss function, the contrastive鈥揷enter loss function, aimed at tightly clustering samples from a similar relationship category in the center of the feature space while dispersing samples from different relationship categories. Through extensive experiments ...
Multi-Head Self-Attention-Enhanced Prototype Network with Contrastive–Center Loss for Few-Shot Relation Extraction by Jiangtao Ma 1,2, Jia Cheng 1, Yonggang Chen 3, Kunlin Li 1, Fan Zhang 4 and Zhanlei Shang 1,* 1 College of Computer and Communication Engineering, Zhengzhou Univers...