790 research outputs found
Generative Adversarial Symmetry Discovery
Despite the success of equivariant neural networks in scientific
applications, they require knowing the symmetry group a priori. However, it may
be difficult to know which symmetry to use as an inductive bias in practice.
Enforcing the wrong symmetry could even hurt the performance. In this paper, we
propose a framework, LieGAN, to automatically discover equivariances from a
dataset using a paradigm akin to generative adversarial training. Specifically,
a generator learns a group of transformations applied to the data, which
preserve the original distribution and fool the discriminator. LieGAN
represents symmetry as interpretable Lie algebra basis and can discover various
symmetries such as the rotation group , restricted Lorentz
group in trajectory prediction and top-quark tagging
tasks. The learned symmetry can also be readily used in several existing
equivariant neural networks to improve accuracy and generalization in
prediction
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed
to process sparse 3D point clouds while preserving spatial arrangements of the
input data. 3D capsule networks arise as a direct consequence of our novel
unified 3D auto-encoder formulation. Their dynamic routing scheme and the
peculiar 2D latent space deployed by our approach bring in improvements for
several common point cloud-related tasks, such as object classification, object
reconstruction and part segmentation as substantiated by our extensive
evaluations. Moreover, it enables new applications such as part interpolation
and replacement.Comment: As published in CVPR 2019 (camera ready version), with supplementary
materia
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