Deep Residual Learning for Image Recognition: A Survey

Deep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond ImageNet.

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• 1 However, the meaning of these impressive numbers and their implications for future research are not fully understood yet.
• 2 In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful implementation in practice represents a significant advance over existing techniques.
• 3 We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond ImageNet.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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