论文标题
Fisher判别三胞胎和训练暹罗网络的对比损失
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
论文作者
论文摘要
暹罗神经网络是一种非常强大的架构，用于提取和度量学习。它通常由几个共享权重的网络组成。暹罗概念是拓扑 - 敏锐的，可以使用任何神经网络作为骨干。训练这些网络的两个最流行的损失功能是三重态和对比损失功能。在本文中，我们提出了两个新型损失函数，称为Fisher Indicant Triplet（FDT）和Fisher判别对比（FDC）。前者使用锚固 - 遥远的三胞胎，而后者则利用成对的锚定和锚定样品。 FDT和FDC损失函数的设计基于Fisher判别分析（FDA）的统计公式，这是一种线性子空间学习方法。我们对MNIST和两个具有挑战性且公开可用的组织病理学数据集的实验显示了拟议损失功能的有效性。
Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese concept is topology-agnostic and can use any neural network as its backbone. The two most popular loss functions for training these networks are the triplet and contrastive loss functions. In this paper, we propose two novel loss functions, named Fisher Discriminant Triplet (FDT) and Fisher Discriminant Contrastive (FDC). The former uses anchor-neighbor-distant triplets while the latter utilizes pairs of anchor-neighbor and anchor-distant samples. The FDT and FDC loss functions are designed based on the statistical formulation of the Fisher Discriminant Analysis (FDA), which is a linear subspace learning method. Our experiments on the MNIST and two challenging and publicly available histopathology datasets show the effectiveness of the proposed loss functions.
