Expect to come across this, one of the most important Python deep learning interview questions for experienced professionals in deep learning, in your next interviews. Yes, CNN has the pooling layer. When it is added to a CNN, yes, Translation invariance is induced when you use pooling. One of the advantages of max-pooling is translation invariance. It provides a form of translation invariance. Invariance means that we can recognize an object as an object, even when its appearance varies in some way. It preserves object’s identity, category etc across the changes in the specifics of visual input, like relative positions of the viewer and object. The translation has a specific meaning in vision, generally borrowed from geometry. It does not refer to any type of conversion, unlike say, a translation from French language to English language or between file formats. Instead, it means that each point/pixel in the image has been moved the same amount in the same direction. Alternately, you can think of the origin as having been shifted an equal amount in the opposite direction. So typically, CNN + max-pooling ~ Translation invariance