_:n8798c06f26af4e279b9770aef7c41418xb47
<http://schema.org/description>
"Equivariance is a nice property to have as it produces much more parameter\nefficient neural architectures and preserves the structure of the input through\nthe feature mapping. Even though some combinations of transformations might\nnever appear (e.g. an upright face with a horizontal nose), current equivariant\narchitectures consider the set of all possible transformations in a\ntransformation group when learning feature representations. Contrarily, the\nhuman visual system is able to attend to the set of relevant transformations\noccurring in the environment and utilizes this information to assist and\nimprove object recognition. Based on this observation, we modify conventional\nequivariant feature mappings such that they are able to attend to the set of\nco-occurring transformations in data and generalize this notion to act on\ngroups consisting of multiple symmetries. We show that our proposed\nco-attentive equivariant neural networks consistently outperform conventional\nrotation equivariant and rotation     reflection equivariant neural networks on\nrotated MNIST and CIFAR-10."
<https://deepai.org/profile/mark-hoogendoorn>