ResNet: Enabling Deep Convolutional Neural Networks through Residual Learning

The introduction of Residual Networks (ResNet) marks a significant advancement in deep learning, particularly in the field of computer vision. Developed by He et al. in 2015, ResNet addresses the challenges of training very deep neural networks, which often suffer from the vanishing gradient problem. By utilizing skip connections, ResNet allows gradients to flow more effectively, enabling the training of networks with hundreds of layers. This breakthrough not only enhances the performance of deep learning models but also opens up new possibilities for complex visual tasks, making it a pivotal development in artificial intelligence.