Revolutionizing Image Recognition and Beyond with Deep Residual Networks

An important advancement in the history of deep learning, which has revolutionized computer vision, is the use of deep residual networks, or ResNets. An extensive overview of ResNets, including their creation, guiding principles, various architectural configurations, and image recognition applications, is given in this review article. We review the underlying theory, clarify the motivations for residual learning, and present empirical evidence of their remarkable effectiveness. We also go over significant advancements, problems that are currently being solved, and possible future paths in the field of deep residual learning for picture identification. In this work, we clarify the fundamentals of Deep Residual Network show they produce their outstanding results and why, when used successfully, they offer a major improvement over existing methods.