Lab41 Reading Group: Deep Residual Learning for Image Recognition

Today’s paper offers a new architecture for Convolution Networks. It was written by He, Zhang, Ren, and Sun from Microsoft Research. I’ll warn you before we start: this paper is ancient. It was published in the dark ages of deep learning sometime at the end of 2015, which I’m pretty sure means its original format was papyrus; thankfully someone scanned it so that future generations could read it. But it is still worth blowing off the dust and flipping through it because the architecture it proposes has been used time and time again, including in some of the papers we have previously read: Deep Networks with Stochastic Depth. He et al. begin by noting a seemingly paradoxical situation: very deep networks perform more poorly than moderately deep networks, that is,…


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