  The development of the back-propagation algorithm represents a landmark in
neural networks. We provide an approach that conducts the back-propagation
again to reverse the traditional back-propagation process to optimize the input
loss at the input end of a neural network for better effects without extra
costs during the inference time. Then we further analyzed its principles and
advantages and disadvantages, reformulated the weight initialization strategy
for our method. And experiments on MNIST, CIFAR10, and CIFAR100 convinced our
approaches could adapt to a larger range of learning rate and learn better than
vanilla back-propagation.

Machine Learning;Neural and Evolutionary Computing