This paper revives Densely Connected Convolutional Networks (DenseNets) and reveals the underrated effectiveness over predominant ResNet-style architectures. We believe DenseNets' potential was overlooked due to untouched training methods and traditional design elements not fully revealing their capabilities. Our pilot study shows dense connections through concatenation are strong, demonstrating that DenseNets can be revitalized to compete with modern architectures. We methodically refine suboptimal components - architectural adjustments, block redesign, and improved training recipes towards widening DenseNets and boosting memory efficiency while keeping concatenation shortcuts. Our models, employing simple architectural elements, ultimately surpass Swin Transformer, ConvNeXt, and DeiT-III - key architectures in the residual learning lineage. Furthermore, our models exhibit near state-of-the-art performance on ImageNet-1K, competing with the very recent models and downstream tasks, ADE20k semantic segmentation, and COCO object detection/instance segmentation. Finally, we provide empirical analyses that uncover the merits of the concatenation over additive shortcuts, steering a renewed preference towards DenseNet-style designs. Our code is available at https://github.com/naver-ai/rdnet.

在这篇论文中，我们研究了密集连接卷积网络（DenseNets）的效果，并发现其在流行的ResNet风格架构中被低估。我们通过串联密集连接的方式展示了DenseNets的优势，并且进行了架构调整、块重设计和改进训练方法以提高DenseNets的性能和内存效率。我们的模型不仅超越了Swin Transformer、ConvNeXt和DeiT-III等关键架构，还在ImageNet-1K等领域展现了接近最新模型的性能，包括ADE20k语义分割和COCO目标检测/实例分割任务。最后，我们提供了以经验为基础的分析结果，揭示了串联连接胜过加法连接的优点，从而引导人们重新偏好DenseNet风格的设计。

DenseNets重装上阵：超越ResNets和ViTs的范式转变