ResNet has been introduced in Deep Residual Learning for Image Recognition, Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, 2015, https://arxiv.org/abs/1512.03385 . This network is very deep and can be trained using a standard stochastic descent gradient by using a standard network component called the residual module, which is then used to compose more complex networks (the composition is called network in network).

In comparison to VGG, ResNet is deeper, but the size of the model is smaller because a global average pooling operation is used instead of full-dense layers.