Deep Learning with Pytorch: Siamese Network

Abstract

"DEEP LEARNING WITH PYTORCH: SIAMESE NETWORK" is a work that addresses person re-identification (re-ID), a difficult computer vision challenge that entails identifying the same person from several camera angles. Because SNNs may learn similarity instead of straight classification, they are becoming a preferred method for this kind of assignment. Using this method, a ranking loss function is optimized by two concurrent CNNs that learn an embedding, or reduced dimensional representation, of the input images. An overview of the procedures involved in person re-identification using SNNs is given in the study, including training, testing, deployment, network architecture, and data preparation. It makes use of the Triplet Ranking Loss function, a popular loss function for SNNs.For similarity-based learning tasks including face recognition, image matching, and document similarity, Siamese Neural Networks are one kind of neural network design that is utilized. The paper offers a thorough tutorial on training a Siamese neural network for a goal based on similarity, namely using the Siamese Neural Network (SNN) to re-identify images taken by different cameras.