Learning to Impute: A General Framework for Semi-supervised Learning

22 December 2019

Papers citing "Learning to Impute: A General Framework for Semi-supervised Learning"

9 / 9 papers shown

Title
Learning to Annotate Part Segmentation with Gradient Matching Yu Yang Xiaotian Cheng Hakan Bilen Xiangyang Ji GAN 21 7 0 06 Nov 2022
Built Year Prediction from Buddha Face with Heterogeneous Labels Yiming Qian Cheikh Brahim El Vaigh Yuta Nakashima B. Renoust Hajime Nagahara Yutaka Fujioka CVBM 24 3 0 02 Sep 2021
Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error Ondrej Bohdal Yongxin Yang Timothy M. Hospedales UQCV OOD 37 21 0 17 Jun 2021
Learning Soft Labels via Meta Learning Nidhi Vyas Shreyas Saxena T. Voice NoLa 19 30 0 20 Sep 2020
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection Fa-Ting Hong Wei-Hong Li Weishi Zheng 34 14 0 16 Apr 2020
Meta-Learning in Neural Networks: A Survey Timothy M. Hospedales Antreas Antoniou P. Micaelli Amos Storkey OOD 38 1,927 0 11 Apr 2020
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average Ben Athiwaratkun Marc Finzi Pavel Izmailov A. Wilson 199 243 0 14 Jun 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 314 11,681 0 09 Mar 2017
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen Harri Valpola OOD MoMe 261 1,275 0 06 Mar 2017