论文标题
反向后退传播以充分利用衍生物
Reverse Back Propagation to Make Full Use of Derivative
论文作者
论文摘要
后传播算法的发展代表了神经网络中的地标。我们提供了一种再次进行背部传播的方法，以扭转传统的背部传播过程，以优化神经网络输入末端的输入损失，从而在推理时间内没有额外的成本，以获得更好的效果。然后，我们进一步分析了其原理，优势和缺点，重新重新制定了我们方法的权重初始化策略。关于MNIST，CIFAR10和CIFAR100的实验说服我们的方法可以适应更大的学习率，并且比香草后传播更好地学习。
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.
