I am trying to make a CNN model . Training the image . Want to know that When we apply kernel on image and take out the features of images. That features are rotation invariant or we have to apply some rotation invariant techniques? . Few person on stack overflow says that max pooling does rotational invariance , some person says that there is rotational invariant CNN architecture . Give me solid reason that how CNN deal with rotational invariant pictures ? Elaborate the answer .
In machine learning , we do some features extraction techniques like SIFT , SURF etc. and apply some algorithm on it, their features are scale and rotation invariant . How about in CNN ?