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Deep Residual Learning for Image Recognition
Abstract
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
1. Introduction
Deep convolutional neural networks [22, 21] have led to a series of breakthroughs for image classification [21, 50, 40]. Driven by the significance of depth, a question arises: Is learning better networks as easy as stacking more layers? An obstacle to answering this question was the notorious problem of vanishing/exploding gradients.
Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer "plain" networks.
When deeper networks are able to start converging, a degradation problem has been exposed: with network depth increasing, accuracy gets saturated and then degrades rapidly.
Deep Residual Learning for Image Recognition
Abstract
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.
1. Introduction
Deep convolutional neural networks [22, 21] have led to a series of breakthroughs for image classification [21, 50, 40].
Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer "plain" networks.
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