Trace ratio criterion-based triple loss function design method

Abstract

The invention discloses a trace ratio criterion-based triple loss function design method. Through investigation and survey of image feature extraction, a triple loss function and a trace ratio criterion, the trace ratio criterion is used as a triple selection criterion and a loss calculation method. The method mainly comprises the steps of A, performing triple sample establishment: establishing triple samples by samples in a data set; B, performing triple sample selection: performing screening in the established triple samples, setting an effective selection mechanism, and while the precisionloss is avoided, increasing the training speed; C, performing loss function design: according to the triple samples obtained in the step B, calculating distances between current samples in triples andpositive and negative samples, and designing the loss function for calculating an error between a model prediction result and a real result; and D, performing deep network training: transmitting themodel error to a deep convolutional neural network, performing update adjustment on network parameters, and iteratively training a model until convergence.

Description

technical field [0001] The invention belongs to the field of loss function design, and in particular relates to a triplet loss function design method based on the trace ratio criterion. The method uses processed image data as the most original input. Background technique [0002] In recent years, with the rise of deep learning, computer vision technology based on convolutional neural networks has developed rapidly. The convolutional neural network is a common deep neural network, and its origin can be traced back to the proposal of the backpropagation algorithm. A conventional neural network consists of an input layer, a hidden layer, and an output layer. Each hidden layer contains several neurons, and each neuron is fully connected to the neurons in the previous layer. The convolutional neural network is different from the conventional neural network. For the case where the input is an image, the neurons in each layer of the network are arranged in three dimensions (width,...

AI Technical Summary

Problems solved by technology

This method uses a fixed parameter α throughout the training process, which cannot achieve the adaptability of the parameters at each iteration, making the generalization ability of the final model insufficient.

Images