@inproceedings{b431151f3cca47ad8c15b2b054d06113,
title = "Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks",
abstract = "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.",
keywords = "Fisher discriminant analysis, Siamese neural network, contrastive loss, feature extraction., triplet loss",
author = "Benyamin Ghojogh and Milad Sikaroudi and Sobhan Shafiei and Tizhoosh, {H. R.} and Fakhri Karray and Mark Crowley",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",
year = "2020",
month = jul,
doi = "10.1109/IJCNN48605.2020.9206833",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings",
}