ResNet: Enabling Deep Convolutional Neural Networks through Residual Learning
This addresses a fundamental bottleneck in deep learning for computer vision, enabling more effective training of deep networks.
The paper tackles the problem of training very deep convolutional neural networks, which suffer from vanishing gradients, by introducing Residual Networks (ResNet) with skip connections, resulting in ResNet-18 achieving 89.9% accuracy on CIFAR-10 compared to 84.1% for a traditional deep CNN.
Convolutional Neural Networks (CNNs) has revolutionized computer vision, but training very deep networks has been challenging due to the vanishing gradient problem. This paper explores Residual Networks (ResNet), introduced by He et al. (2015), which overcomes this limitation by using skip connections. ResNet enables the training of networks with hundreds of layers by allowing gradients to flow directly through shortcut connections that bypass intermediate layers. In our implementation on the CIFAR-10 dataset, ResNet-18 achieves 89.9% accuracy compared to 84.1% for a traditional deep CNN of similar depth, while also converging faster and training more stably.