In mathematics, equivariance is a form of symmetry for functions from one space with symmetry to another (such as symmetric spaces).

Basically we have convolutional layers that are supposed to be shift equivariant and pooling layers that are approximately shift invariant. So why is that. 98 ...

18 мар. 2024 г. · In this tutorial, we'll explain two common concepts used in computer vision: translation invariance and translation equivariance.

11 нояб. 2022 г. · In this paper, we propose an alternative that avoids this architectural constraint by learning to produce canonical representations of the data.

8 дек. 2020 г. · Equivariance has a remarkable ability to simplify our understanding of neural networks. When we see neural networks as families of features, ...

21 нояб. 2023 г. · The goal of the current post is to clarify the mutual relation between equivariance and the convolutional network design.

Equivariance in a mathematical context refers to a condition where a function provides the same output albeit with a different order when the order of the input ...

CNNs are famously equivariant with respect to translation. This means that translating the input to a convolutional layer will result in translating the output.

Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations. In.

7 нояб. 2023 г. · Equivariance ensures the consistency of predictions under symmetry transformations: an equivariant model generalizes anything it learns to all ...