Quantization and Training of Neural Networks for Efficient . . . - GitHub Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference Author: Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, Dmitry Kalenichenko Origin: https: a
360 video stabilization: A new algorithm for smoother 360 . . . - GitHub joyhuang9473 opened this issue Sep 3, 2016 · 0 comments Open 360 video stabilization: Sign up for free to join this conversation on GitHub Already have an account? Sign in to comment Assignees No one assigned Labels None yet Projects None yet Milestone No milestone Development
Photo-Realistic Single Image Super-Resolution Using a . . . - GitHub Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Author: Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz,
Heterogeneous Knowledge Transfer in Video Emotion Recognition . . . - GitHub joyhuang9473 opened this issue Jun 6, 2017 · 0 comments Open Heterogeneous Knowledge Transfer in Video Emotion Recognition, Sign up for free to join this conversation on GitHub Already have an account? Sign in to comment Assignees No one assigned Labels Emotion Estimation Face Attribute Estimation Projects
Local Binary Convolutional Neural Networks - GitHub Local Binary Convolutional Neural Networks Author: Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides Origin: https: arxiv org abs 1608 06049 Related: https
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node . . . - GitHub joyhuang9473 opened this issue Jun 17, 2020 · 0 comments Open Sign up for free to join this conversation on GitHub Already have an account? Sign in to comment Assignees No one assigned Labels GCN Projects None yet Milestone No milestone Development No branches or pull requests
deepid-implementation README. md at master · joyhuang9473 . . . - GitHub DeepID-implementation is an implementation of paper "Deep Learning Face Representation from Predicting 10,000 Classes", which proposes to learn a set of compact, 160-dims high level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face