ResNet, short for "Residual Network," is a type of deep neural network architecture that was introduced by Microsoft researchers in 2015. ResNet is designed to address the problem of vanishing gradients, which can occur in deep neural networks that are many layers deep. The main innovation in ResNet is the use of residual connections, also known as skip connections. These connections allow information from earlier layers of the network to bypass some of the later layers and be directly fed into the later layers. This helps to ensure that the gradient signal from the output can propagate back through the network during training, which can help to prevent the vanishing gradient problem. ResNet has been shown to be very effective at image recognition and other computer vision tasks. It has achieved state-of-the-art performance on a number of benchmark datasets, such as ImageNet. Since its introduction, many variations and improvements to the original ResNet architecture have been proposed, including ResNeXt, Wide ResNet, and Residual Attention Network (RANet).