Learning Invariant Riemannian Geometric Representations Using Deep Nets-Reference-Cited by-同舟云学术

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Learning Invariant Riemannian Geometric Representations Using Deep Nets

Published:2017-10 Issue: Volume: Page:
ISSN:
Container-title:2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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Author:

Lohit Suhas,Turaga Pavan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8234943/8265191/08265368.pdf?arnumber=8265368

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Orthogonality and graph divergence losses promote disentanglement in generative models;Frontiers in Computer Science;2024-05-22

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3. MMFF: Multi-manifold feature fusion based neural networks for target recognition in complex-valued SAR imagery;ISPRS Journal of Photogrammetry and Remote Sensing;2021-10

4. RGGNet: Tolerance Aware LiDAR-Camera Online Calibration With Geometric Deep Learning and Generative Model;IEEE Robotics and Automation Letters;2020-10

5. Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping;2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2019-06

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