32 citations · 57 across the 3 of their papers we have counts for
7 papers
Learning Equivariant Representations
Carlos Esteves
State-of-the-art deep learning systems often require large amounts of data and computation. For this reason, leveraging known or unknown structure of the data is paramount. Convolu…
An Analysis of SVD for Deep Rotation Estimation
Jake Levinson, Carlos Esteves, Kefan Chen +4
Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto or . These tools have long been used for a…
Spin-Weighted Spherical CNNs
Carlos Esteves, Ameesh Makadia, Kostas Daniilidis
Learning equivariant representations is a promising way to reduce sample and model complexity and improve the generalization performance of deep neural networks. The spherical CNNs…
Theoretical Aspects of Group Equivariant Neural Networks
Carlos Esteves
Group equivariant neural networks have been explored in the past few years and are interesting from theoretical and practical standpoints. They leverage concepts from group represe…
Equivariant Multi-View Networks
Carlos Esteves, Yinshuang Xu, Christine Allen-Blanchette +1
Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation…
Cross-Domain 3D Equivariant Image Embeddings
Carlos Esteves, Avneesh Sud, Zhengyi Luo +2
Spherical convolutional networks have been introduced recently as tools to learn powerful feature representations of 3D shapes. Spherical CNNs are equivariant to 3D rotations makin…