  Deep learning has made significant progress in computer vision, specifically
in image classification, object detection, and semantic segmentation. The skip
connection has played an essential role in the architecture of deep neural
networks,enabling easier optimization through residual learning during the
training stage and improving accuracy during testing. Many neural networks have
inherited the idea of residual learning with skip connections for various
tasks, and it has been the standard choice for designing neural networks. This
survey provides a comprehensive summary and outlook on the development of skip
connections in deep neural networks. The short history of skip connections is
outlined, and the development of residual learning in deep neural networks is
surveyed. The effectiveness of skip connections in the training and testing
stages is summarized, and future directions for using skip connections in
residual learning are discussed. Finally, we summarize seminal papers, source
code, models, and datasets that utilize skip connections in computer vision,
including image classification, object detection, semantic segmentation, and
image reconstruction. We hope this survey could inspire peer researchers in the
community to develop further skip connections in various forms and tasks and
the theory of residual learning in deep neural networks. The project page can
be found at https://github.com/apple1986/Residual_Learning_For_Images
