I am reading the paper with the title "Classification of indoor actions through deep neural networks". And I came across this statement:
With this mechanism very complex functions can be learned combining these modules: the resulting networks are often very sensitive to minute details and insensitive to large irrelevant variations.
I have read a lot about deep neural network. However, I might have missed the logic behind why deep neural networks are insensitive to large irrelevant features.