200 citations · 311 across the 21 of their papers we have counts for
4 papers · 1 filter
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks
Huu Le, Rasmus Kjær Høier, Che-Tsung Lin +1
We propose a new algorithm for training deep neural networks (DNNs) with binary weights. In particular, we first cast the problem of training binary neural networks (BiNNs) as a bi…
BabelCalib: A Universal Approach to Calibrating Central Cameras
Yaroslava Lochman, Kostiantyn Liepieshov, Jianhui Chen +3
Existing calibration methods occasionally fail for large field-of-view cameras due to the non-linearity of the underlying problem and the lack of good initial values for all parame…
Bilevel Programs Meet Deep Learning: A Unifying View on Inference Learning Methods
Christopher Zach
In this work we unify a number of inference learning methods, that are proposed in the literature as alternative training algorithms to the ones based on regular error back-propaga…
Escaping Poor Local Minima in Large Scale Robust Estimation
Huu Le, Christopher Zach
Robust parameter estimation is a crucial task in several 3D computer vision pipelines such as Structure from Motion (SfM). State-of-the-art algorithms for robust estimation, howeve…