DenseNet

DenseNet connects every layer in a network to subsequent layers, allowing features learned earlier to be reused throughout the model. Instead of each layer receiving information only from the layer immediately before it, DenseNet combines outputs from multiple preceding layers. This dense connectivity can improve information flow and reduce the need to relearn similar features.

DenseNet helps address challenges associated with training very deep neural networks, including vanishing gradients and inefficient feature propagation. Its feature reuse can improve learning efficiency while reducing the number of parameters required compared with some traditional deep network architectures.