Figure 2. Training Loss and Visualization of Deep Features at Different Training Iterations.

A, Training loss with accuracy with training iterations. B, Feature clustering with the progress of training. The dimensionality of deep features was nonlinearly reduced by the t-distributed stochastic neighbor embedding (t-SNE) method for visualization.