What advantage does the SELU activation function offer over traditional activation functions?

The SELU (Scaled Exponential Linear Unit) activation function offers self-normalization, which is a significant advantage over traditional activation functions. This property means that when the inputs to the network are processed through layers activated by SELU, the outputs tend to maintain a mean of zero and a variance of one, facilitating better training dynamics.

Self-normalization occurs because the SELU function is designed such that if the activations are centered around zero and the variance is maintained, the subsequent layers in the network will also produce outputs that maintain this statistical property. This natural characteristic helps in stabilizing the learning process, eliminating the need for explicit normalization layers, like batch normalization, which can add complexity and computational overhead. As a result, networks using SELU activations can often achieve faster training with fewer training epochs, making it particularly advantageous for deep learning architectures.

Additionally, the self-normalizing nature of SELU allows for more robust training and helps to alleviate problems related to the training stability that arise in deeper networks, a common issue with traditional activation functions like ReLU or sigmoid, which do not inherently provide this feature.