DenseNet is a type of convolutional neural network (CNN) architecture designed to improve the flow of information and gradients throughout the network. It connects each layer to every other layer in a feed-forward manner, allowing for better feature reuse and reducing the number of parameters needed. This connectivity pattern helps in training deeper networks more effectively. The architecture consists of dense blocks, where each layer receives input from all preceding layers. This design not only enhances performance on tasks like image classification but also mitigates issues like vanishing gradients, making DenseNet a popular choice in deep learning applications.