Scale Equivariant Neural Networks with Morphological Scale-Spaces
Mateus Sangalli,
Samy Blusseau,
Santiago Velasco-Forero
et al.
Abstract:The translation equivariance of convolutions can make convolutional neural networks translation equivariant or invariant. Equivariance to other transformations (e.g. rotations, affine transformations, scalings) may also be desirable as soon as we know a priori that transformed versions of the same objects appear in the data. The semigroup cross-correlation, which is a linear operator equivariant to semigroup actions, was recently proposed and applied in conjunction with the Gaussian scale-space to create archi… Show more
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