The ReLU function plays a crucial role in deep learning models by introducing non-linearity without affecting the positive inputs, which helps prevent the vanishing gradient problem often encountered with other activation functions like sigmoid or tanh. Its simplicity leads to better performance and faster convergence in many deep learning tasks, as it allows the network to learn complex patterns more effectively. ReLU is particularly beneficial because it does not activate all neurons at the same time, meaning that only a subset of neurons is active, providing sparse representations that are computationally efficient.
Historical Overview: The ReLU function was first introduced within the context of neural networks in 2000 but gained significant popularity after 2010 when it was shown to greatly improve the training of deep neural networks.
Key Contributors: Although the concept of rectified units dates back further, the form of ReLU as commonly used in deep learning was popularized by Geoffrey Hinton and his colleagues, who demonstrated its effectiveness in the paper "Deep Sparse Rectifier Neural Networks" during the 2010s. This work was foundational in advancing the use of deep learning architectures.