In machine learning, an activation function is applied to the output of each neuron in a neural network. The exponential linear unit (ELU) is an activation function that is commonly used in neural networks.
Mean Unit Activations
ELUs have negative values which allows them to push mean unit activations closer to zero. This is similar to batch normalization, but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect.
Noise-Robust Deactivation State
While other activation functions like Leaky ReLUs (LReLUs) and Parameterized ReLUs (PReLUs) have negative values, they do not ensure a noise-robust deactivation state. ELUs, on the other hand, saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information.
The ELU Function
The exponential linear unit (ELU) function is defined in the following way:
f(x) = x if x > 0
α(exp(x) − 1) if x ≤ 0
Here, x is the input to the function and α is a positive constant.
Benefits of Using ELUs
The main benefit of using ELUs is that they are noise-robust and can handle noisy input data. Additionally, because ELUs push mean unit activations towards zero, they can lead to faster training of neural networks.
Research has shown that ELUs perform better than other activation functions like ReLUs, LReLUs, and PReLUs on a variety of tasks. However, the choice of activation function ultimately depends on the specific problem and the neural network architecture being used.
