What type of activation functions are preferred to avoid saturation in neural networks?

Non-saturating nonlinear activation functions are preferred because they help mitigate the issue of saturation that can occur in neural networks during training. Saturation refers to the phenomenon where neurons become less sensitive to input changes, leading to very small gradients during backpropagation, which can stall or slow down learning significantly.

Functions such as ReLU (Rectified Linear Unit) and its variants do not saturate in the positive domain, meaning that they maintain a constant gradient (usually 1) during forward propagation for positive inputs. This property allows for faster convergence during training and helps prevent issues associated with vanishing gradients, enabling the effective training of deeper networks.

In contrast, linear activation functions, threshold-based activation functions, and softmax functions have characteristics that either lead to saturation problems or are not suitable for hidden layers in deep networks. For example, linear activation functions lead to similar gradients regardless of input, thus failing to capture complex patterns in data. Softmax is specifically used in the output layer for multi-class classification tasks and is not meant for hidden layers where nonlinearities are needed to model complex relationships. Therefore, non-saturating nonlinear activation functions are the preferred choice to enhance the learning capacity of neural networks.