What is auxiliary loss that is mentioned in the PSPNet(Pyramid Scene Parsing Network) paper link ?
I'm quoting the part of the paper down below
An example of our deeply supervised ResNet101  model is illustrated in Fig. 4. Apart from the main branch using softmax loss to train the final classifier, another classifier is applied after the fourth stage, i.e., the res4b22 residue block. Different from relay backpropagation  that blocks the backward auxiliary loss to several shallow layers, we let the two loss functions pass through all previous layers. The auxiliary loss helps optimize the learning process, while the master branch loss takes the most responsibility. We add weight to balance the auxiliary loss.
My question is how does this auxiliary loss work and how does it help in training process. What is its work in the network ?