Deep Supervision refers to the training strategy where intermediate layers of a deep neural network are connected to the loss function. In traditional deep learning, the loss is often applied only at the output layer. Deep supervision involves applying the loss function at multiple intermediate layers, which can facilitate better learning and representation of features.