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Found 8 papers, 3 papers with code
Moving Frame Net: SE(3)-Equivariant Network for Volumes
Equivariance of neural networks to transformations helps to improve their performance and reduce generalization error in computer vision tasks, as they apply to datasets presenting symmetries (e. g. scalings, rotations, translations).
Scale Equivariant U-Net
Therefore, this paper introduces the Scale Equivariant U-Net (SEU-Net), a U-Net that is made approximately equivariant to a semigroup of scales and translations through careful application of subsampling and upsampling layers and the use of aforementioned scale-equivariant layers.
Morphological adjunctions represented by matrices in max-plus algebra for signal and image processing
In discrete signal and image processing, many dilations and erosions can be written as the max-plus and min-plus product of a matrix on a vector.
Differential invariants for SE(2)-equivariant networks
Symmetry is present in many tasks in computer vision, where the same class of objects can appear transformed, e. g. rotated due to different camera orientations, or scaled due to perspective.
Scale Equivariant Neural Networks with Morphological Scale-Spaces
The translation equivariance of convolutions can make convolutional neural networks translation equivariant or invariant.
Part-based approximations for morphological operators using asymmetric auto-encoders
This paper addresses the issue of building a part-based representation of a dataset of images.
Max-plus Operators Applied to Filter Selection and Model Pruning in Neural Networks
Following recent advances in morphological neural networks, we propose to study in more depth how Max-plus operators can be exploited to define morphological units and how they behave when incorporated in layers of conventional neural networks.
Psychophysics, Gestalts and Games
Using a computational quantitative version of the non-accidentalness principle, we raise the possibility that the psychophysical and the (older) gestaltist setups, both applicable on dot or Gabor patterns, find a useful complement in a Turing test.
