Основные понятия
Residual learning with skip connections plays a crucial role in the architecture of deep neural networks, enabling easier optimization through residual learning during the training stage and improving accuracy during testing.
Аннотация
This survey provides a comprehensive overview of the development of skip connections and residual learning in deep neural networks. It outlines the short history of skip connections, surveys the evolution of residual learning in deep neural networks, and summarizes the effectiveness of skip connections in the training and testing stages.
The key highlights include:
The origin of skip connections and residual learning, tracing back to negative feedback systems and residual representations in image processing.
The introduction of ResNet, which reformulates the layers as learning residual functions with reference to the inputs by introducing skip connections, enabling easier optimization and continuous performance gain with increasing depth.
The development of skip connections, including the integration of short and long skip connections, widening the residual units, strengthening the ability to learn discriminative features, making ResNet-like models more efficient, and incorporating self-attention mechanisms.
Theoretical explanations for the effectiveness of skip connections, such as improved information flow, ensemble learning properties, regularization effects, and elimination of singularities.
A summary of seminal papers, source code, models, and datasets that utilize skip connections in computer vision tasks, including image classification, object detection, semantic segmentation, and image reconstruction.
This survey aims to inspire peer researchers to further explore skip connections in various forms and tasks, as well as the theory of residual learning in deep neural networks.
Статистика
"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."
Цитаты
"Skip connection, also known as shortcut connection, has been studied for a long time."
"Compared to learning the unreferenced feature maps, it becomes easier to optimize the residual features and achieve continuous gain with increasing depth."
"The main motivation of the ResNet is different. In ResNet, the layers are reformulated as learning residual functions with reference to the inputs by introducing a skip connection."