Circle-Based Ratio Loss for Person Reidentification

Figure 2

MNIST experiment results with the original softmax, normalized softmax, and normalized softmax with ratio loss, respectively. For the intuitive demonstration, we use a subset of MNIST for the experiments and 2000 training samples of each class are used to train the model. By setting the output dimension of the last feature layer as 2, the learned features can be visualized in 2D space, where the x-axis and y-axis correspond to the two dimensions of the learned features. In the figure, the first row gives the distributions of the original features in 2D space and the second row gives the corresponding normalized features. Best viewed in color. (a) Original softmax. (b) Normalized softmax. (c) Normalized softmax with ratio loss.