What are the pros and cons of using sigmoid or softmax approach when dealing with 2 classes? I know that when using Sigmoid, you only need 1 output neuron (binary classification) and for Softmax - it's 2 neurons (multiclass classification). But for performance improvement (if there is one), is there any difference which of these 2 approaches works better, or when would you recommend using one over the other. Or maybe there are certain situations when using one of these is better than the other.
Any comments or shared experience will be appreciated.
Sigmoid is used for binary cases and softmax is its  generalized version for multiple classes. But, essentially what they do is over exaggerate the distances between the various values.
If you have values on a unit sphere, apply sigmoid or softmax on those values would lead to the points going to the poles of the sphere.