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This paper presents a novel method to distill knowledge from a deep pose regressor network for efficient Visual Odometry (VO).
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Binarized Neural Networks
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
J. Yim, D. Joo, J. Bae, and J. Kim · 2017
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Model compression and acceleration for deep neural networks: The principles, progress, and challenges
Y. Cheng, D. Wang, P. Zhou, and T. Zhang · 2018
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Self-supervised knowledge distillation using singular value decomposition
S. H. Lee, D. H. Kim, and B. C. Song · 2018
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Model compression via distillation and quantization
A. Polino, R. Pascanu, and D. Alistarh · 2018
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Visual SLAM and Structure from Motion in Dynamic Environments : A Survey
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Convolutional neural networks with low-rank regularization
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Progressive blockwise knowledge distillation for neural network acceleration
H. Wang, H. Zhao, X. Li, and X. Tan · 2018
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End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
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Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
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Deeptam: Deep tracking and mapping
H. Zhou, B. Ummenhofer, and T. Brox · 2018
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Ganvo: Unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks
Y. Almalioglu, M. R. U. Saputra, P. P. B. d. Gusmao, A. Markham, and N. Trigoni · 2019
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Learning monocular visual odometry through geometry-aware curriculum learning
M. R. U. Saputra, P. P. B. d. Gusmao, S. Wang, A. Markham, and N. Trigoni · 2019
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