The 'Vanishing Gradient Problem' is most commonly associated with which types of activation functions in deep networks?

Sigmoid or Tanh

Saturating activations like Sigmoid have very small derivatives for high or low input values. During backpropagation, multiplying these small gradients across many layers causes the gradient to decrease exponentially, effectively stopping the early layers from learning.