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The shortcuts prove to be extremely helpful in the training process, and it significantly reduces the resulting training error.
![deep residual learning for image recognition deep residual learning for image recognition](https://ww2.mathworks.cn/help/examples/deeplearning_shared/win64/GestureRecognitionUsingSlowFastVideoClassificationExample_01.png)
The authors performed extensive experiments to compare the same network with and without the shortcuts, and also observe the behavior when the depth of the network reaches 1,000 layers. As a result, the output neuron only needs to learn the residual that is \(F(x) = H(x) - x\). The identity shortcut introduces the term \(x\) to the output and makes the output function take the form of \(F(x) + x\). A typical building block of ResNet looks like this: A basic building block of ResNet. ResNet attempts to remedy this problem by introducing shortcuts that skip one or more layers. Experiments show increased training and testing errors with respect to the depth of the network. However, even with normalization, experiments still show that the training error usually increases with the number of layers, indicating there is an increased difficulty in parameter optimization. The convergence issue comes from vanishing/exploding gradients, which can be largely addressed by normalizing initialization and inserting Batch Norm layers between regular network layers. However, a deeper model usually has difficulty converging. Theoretically, a more complex model should be able to fit the training data better and achieve a lower training error. As a result, ResNet won many computer vision competitions in 2015 and has been proven to be extremely powerful.Īs the number of layers in a CNN increases, the model complexity increases. By adding more layers, more fine-grained levels of features can be extracted and processed. From the input to the output, each layer of the network is essentially performing low to high-level feature extraction. It is widely recognized that the increased depths in deep convolutional neural networks (CNNs) are extremely helpful in computer vision tasks. As a comparison, VGG, the previous state-of-the-art network proposed in 2014, has only 16 layers. By introducing identity shortcut connections in the network architecture, the network depth can easily reach 152 layers and still remain easy to solve. ResNet is proposed in the 2015 paper Deep Residual Learning for Image Recognition to solve the problem of the increasing difficulty to optimize parameters in deeper neural networks. Deep residualnetsarefoundationsofoursubmissionstoILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.ResNet: Deep Residual Learning for Image Recognition (CVPR 2016 Paper) Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. The depth of representations is of central importance for many visual recognition tasks. We also present analysis on CIFAR-10 with 1 layers. Thisresultwonthe1stplaceonthe ILSVRC 2015 classification task.
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Anensembleoftheseresidualnetsachieves3.57%error ontheImageNettestset. On the ImageNet dataset we evaluate residualnets with adepth of up to152 layers-8× deeper than VGG nets but still having lower complexity.
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We provide comprehensive empirical evidence showing that these residual networksareeasiertooptimize,andcangainaccuracyfrom considerably increased depth. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. Deeper neural networks are more difficult to train.