Abstract Group equivariance (eg SE (3) equivariance) is a critical physical symmetry in
science, from classical and quantum physics to computational biology. It enables robust and …

Convolutional neural networks benefit from translation equivariance achieving tremendous
success. Equivariant networks further extend this property to other transformation groups …

Attention mechanism has shown great performance and efficiency in a lot of deep learning
models, in which relative position encoding plays a crucial role. However, when introducing …

We present a PDE-based framework that generalizes Group equivariant Convolutional
Neural Networks (G-CNNs). In this framework, a network layer is seen as a set of PDE …

This paper presents a hybrid approach between scale-space theory and deep learning,
where a deep learning architecture is constructed by coupling parameterized scale-space …

Convolutional neural networks (CNNs) have dominated the field of Computer Vision and
achieved great success due to their built-in translation equivariance. Group equivariant …

The deep unfolding approach has attracted significant attention in computer vision tasks,
which well connects conventional image processing modeling manners with more recent …

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 …

Sparse-view computed tomography (CT) has been adopted as an important technique for
speeding up data acquisition and decreasing radiation dose. However, due to the lack of …

It has been shown that equivariant convolution is very helpful for many types of computer
vision tasks. Recently, the 2D filter parametrization technique has played an important role …