About: SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
Creator	Fischer, Volker Fuchs, Fabian Welling, Max Worrall, Daniel
source	ArXiv
abstract	We introduce the SE(3)-Transformer, a variant of the self-attention module for 3D point clouds, which is equivariant under continuous 3D roto-translations. Equivariance is important to ensure stable and predictable performance in the presence of nuisance transformations of the data input. A positive corollary of equivariance is increased weight-tying within the model, leading to fewer trainable parameters and thus decreased sample complexity (i.e. we need less training data). The SE(3)-Transformer leverages the benefits of self-attention to operate on large point clouds with varying number of points, while guaranteeing SE(3)-equivariance for robustness. We evaluate our model on a toy $N$-body particle simulation dataset, showcasing the robustness of the predictions under rotations of the input. We further achieve competitive performance on two real-world datasets, ScanObjectNN and QM9. In all cases, our model outperforms a strong, non-equivariant attention baseline and an equivariant model without attention.
has issue date	2020-06-18(xsd:dateTime)
has license	arxiv
sha1sum (hex)	6075091294a0fe0fe5c6c7b7a0df9029b6a965cb
resource representing a document's title	SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
resource representing a document's body	covid:6075091294a0fe0fe5c6c7b7a0df9029b6a965cb#body_text
is schema:about of	named entity 'model' named entity 'corollary' named entity 'Center' named entity 'data' named entity 'robustness' »more»

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