Abstract
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 this https URL
Abstract (translated)
深度学习在计算机视觉领域取得了显著进展，尤其是在图像分类、目标检测和语义分割方面。跳转连接在深度神经网络的架构中发挥了关键作用，通过在训练阶段通过残差学习进行更简单的优化，并在测试阶段提高准确性。许多神经网络都继承了残差学习与跳转连接的想法，将其作为设计神经网络的标准选择。 本次调查对跳转连接在深度神经网络中的发展进行了全面的概括和展望。首先简要介绍了跳转连接的短史，然后调查了在深度神经网络中残差学习的开发。总结了跳转连接在训练和测试阶段的有效性，并讨论了在残差学习中将跳转连接用于未来研究的方向。最后，我们总结了在计算机视觉领域使用跳转连接的一些论文、源代码、模型和数据集。我们希望能激励社区中的同行研究者在各种形式和任务上进一步发展跳转连接，并探讨深度神经网络中残差学习的理论。项目页面可以通过这个链接找到：https://github.com/your_username/project_name
URL
https://arxiv.org/abs/2405.01725
