Deeper neural networks are hard to train. The paper presents a learning framework which eases the training for networks which are substantially deeper
The paper provides a method of learning referenced functions (? probably a function which links layers) rather than old types of unreferenced functions
The paper provides evidence that these residues help networks optimize and gain accuracy from increased depth.
Analysis of CIFAR-10|100 datasets is also presented in the paper with 100 and 1000 layers
The paper argues that depth of representations is very important for many visual recognition tasks. The paper says that it obtained a 28% relative improvement on COCO object detection
This paper won 1st places on the tasks of ImageNet detection, localisation, COCO detection and segmentation.
(Example Image) Network architectures for ImageNet
Left : VGG-16
Middle: Plain 34 parameter layers
Right: Residual Network with 34 parameter layers