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What is time translation invariance?
Time translation symmetry or temporal translation symmetry (TTS) is a mathematical transformation in physics that moves the times of events through a common interval. In mathematics, the set of all time translations on a given system form a Lie group.
Is translation a symmetry operation?
…these symmetry elements, there are translations and symmetry operations combined with translations. (Translation is the operation in which a motif is repeated in a linear pattern at intervals that are equal to the translation distance [commonly on the 1 to 10 Å level].)
What’s the difference between translational invariance and equivariance?
Translational Invariance and Translational Equivariance are frequently confused as the same thing but are different properties of CNN. To understand the difference please read below. Convolutional Neural Networks have been the go-to architecture for Image and video-based tasks like classification, localization, segmentation, etc.
Which is the result of translational invariance in CNN?
Translational Invariance makes the CNN invariant to translation. Invariance to translation means that if we translate the inputs the CNN will still be able to detect the class to which the input belongs. Translational Invariance is a result of the pooling operation. In a traditional CNN architecture, there are three stages.
What does it mean to be invariant to translation?
Invariant to translation means that a translation of input features doe not change the outputs at all. So if your pattern 0,3,2,0,0 on the input results in 0,1,0 in the output, then the pattern 0,0,3,2,0 would also lead to 0,1,0 For feature maps in convolutional networks to be useful, they typically need both properties in some balance.
What is translation invariance in a convolutional layer?
While your answer is correct in part and leads to confusion. It is true that Convolutional layers themselves or output feature maps are translation equivariant. What the max-pooling layers do is provide some translation invariance as @Matt points out.