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Found 6 papers, 3 papers with code
Equivariant Neural Tangent Kernels
Equivariant neural networks have in recent years become an important technique for guiding architecture selection for neural networks with many applications in domains ranging from medical image analysis to quantum chemistry.
Emergent Equivariance in Deep Ensembles
We show that deep ensembles become equivariant for all inputs and at all training times by simply using data augmentation.
HEAL-SWIN: A Vision Transformer On The Sphere
High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving.
Diffeomorphic Counterfactuals with Generative Models
Counterfactuals can explain classification decisions of neural networks in a human interpretable way.
Equivariance versus Augmentation for Spherical Images
We compare the performance of the group equivariant networks known as S2CNNs and standard non-equivariant CNNs trained with an increasing amount of data augmentation.
Geometric Deep Learning and Equivariant Neural Networks
We also discuss group equivariant neural networks for homogeneous spaces $\mathcal{M}=G/K$, which are instead equivariant with respect to the global symmetry $G$ on $\mathcal{M}$.
