  We introduce collaborative learning in which multiple classifier heads of the
same network are simultaneously trained on the same training data to improve
generalization and robustness to label noise with no extra inference cost. It
acquires the strengths from auxiliary training, multi-task learning and
knowledge distillation. There are two important mechanisms involved in
collaborative learning. First, the consensus of multiple views from different
classifier heads on the same example provides supplementary information as well
as regularization to each classifier, thereby improving generalization. Second,
intermediate-level representation (ILR) sharing with backpropagation rescaling
aggregates the gradient flows from all heads, which not only reduces training
computational complexity, but also facilitates supervision to the shared
layers. The empirical results on CIFAR and ImageNet datasets demonstrate that
deep neural networks learned as a group in a collaborative way significantly
reduce the generalization error and increase the robustness to label noise.
