DenseNet Paper Walkthrough: All Connected

DenseNet is a deep learning architecture designed to solve the vanishing gradient problem by creating dense connections where each layer receives feature maps from all preceding layers. Unlike ResNet which uses element-wise summation, DenseNet concatenates feature maps channel-wise, which promotes feature reuse and improves information flow. The architecture consists of Dense Blocks, where these connections occur, and Transition Layers, which perform downsampling and channel reduction via a compression factor. This design makes DenseNet more parameter-efficient than traditional convolutional networks, with variants like DenseNet-BC using bottleneck layers to further improve computational efficiency.