Stimulative Training of Residual Networks: ASocial Psychology Perspective of Loafing

We further verify that stimulative training can well handle the loafing problem on different datasets and residual networks. As shown in Fig. r1, we can see that stimulative training can always improve the performance of a given residual network and all of its sub-networks by a larger margin on various residual networks and benchmark datasets. In other words, different residual networks trained on different datasets invariably suffer from the problem of network loafing, which can be well solved by the proposed stimulative training strategy. Figure r1: Stimulative training can improve the performance of a given residual network and all of its sub-networks significantly. We further verify it on various residual networks and benchmark datasets.