FaceNet

Basic structure The structure of FaceNet is represented schematically in Figure 1. For training, researchers used input batches of about 1800 images. For each identity represented in the input batches, there were 40 similar images of that identity and several randomly selected images of other identities. These batches were fed to a deep convolutional neural network, which was trained using stochastic gradient descent with standard backpropagation and the Adaptive Gradient Optimizer (AdaGrad) algorithm. The learning rate was initially set at 0.05, which was later lowered while finalizing the model. Structure of the CNN The researchers used two types of architectures, which they called NN1 and NN2, and explored their trade-offs. The practical differences between the models lie in the difference of parameters and FLOPS. The details of the NN1 model are presented in the table below. Triplet loss A key innovation of the system was the triplet loss function and its associated mining method. This function has since become central in a variety of other one-shot learning problems. ==Performance==