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I'm somewhat new to equivariant neural networks so this question might be naive. I wanted to see if it's possible to maintain equivariance if only part of the input is transformed i.e. a transformation of a fraction of the feature outputs of the network occurs from a partial transformation of the input. Is there some notion of locality and maintaining equivariance?
I'm asking because I have a model that produces factorized feature vectors for input images, so I had this intuition that a transformation on part of the image that only effects one feature vector might maintain equivariance given a transformation from a symmetry group and an equivariant NN. Hope the question makes sense!
The text was updated successfully, but these errors were encountered:
Hey folks,
I'm somewhat new to equivariant neural networks so this question might be naive. I wanted to see if it's possible to maintain equivariance if only part of the input is transformed i.e. a transformation of a fraction of the feature outputs of the network occurs from a partial transformation of the input. Is there some notion of locality and maintaining equivariance?
I'm asking because I have a model that produces factorized feature vectors for input images, so I had this intuition that a transformation on part of the image that only effects one feature vector might maintain equivariance given a transformation from a symmetry group and an equivariant NN. Hope the question makes sense!
The text was updated successfully, but these errors were encountered: