Hands-On Deep Learning for IoT

Residual neural networks

Since there are sometimes millions and millions of hyperparameters and other practical aspects, it's really difficult to train deeper neural networks. To overcome this limitation, Kaiming H. et al. ( https://arxiv.org/abs/1512.03385v1) proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously.

They also explicitly reformulated the layers as learning residual functions with reference to the layer inputs, instead of learning non-referenced functions. This way, these residual networks are easier to optimize and can gain accuracy from considerably increased depth. The downside is that building a network by simply stacking residual blocks inevitably ...