Published Paper Details:

Training deep neural networks becomes increasingly challenging as their depth grows. To address this, we introduce a residual learning framework that simplifies the training of networks significantly deeper than those traditionally used. Rather than having each layer learn a direct mapping, we reformulate the layers to learn residual functions--essentially focusing on the difference from the input, which eases optimization. Our experiments provide strong evidence that these residual networks (ResNets) are easier to train and benefit from increased depth. On the ImageNet dataset, we tested ResNets with up to 152 layers, which is eight times deeper than VGG networks [41], yet they remain less complex in terms of computation. An ensemble of these deep ResNets achieved a 3.57% error rate, securing first place in the ILSVRC 2015 classification task. We also analyzed performance on CIFAR-10, experimenting with networks up to 100 and 1000 layers deep. Our findings show that deeper representations are crucial for visual recognition tasks. Thanks to the extreme depth of our models, we achieved a 28% relative improvement on the COCO object detection dataset. These deep residual networks were also key to our top-ranking entries in the ILSVRC and COCO 2015 competitions, winning first place in multiple tasks including ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.