Unsupervised or Indirectly Supervised Learning
title
For instance, data augmentation consistency regularization [Sohn et al., 2020, Berthelot et al., 2019] generates pseudo-labels from the current model predictions on
Dual Swap Disentangling
Zunlei Feng, Xinchao Wang, Chenglong Ke, An-Xiang Zeng, Dacheng Tao, Mingli Song
Neural Information Processing Systems http://nips.cc/
Realistic Evaluationof Deep Semi-Supervised Learning Algorithms
Further, thetrainingprocedure (optimizer, numberoftrainingsteps, learningratedecayschedule, etc.) is notstandardized.
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Neural Information Processing Systems http://nips.cc/
Unsupervised Depth Estimation, 3D Face Rotation and Replacement
Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Chris Pal
Neural Information Processing Systems http://nips.cc/
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
Pedro Mercado, Francesco Tudisco, Matthias Hein
Neural Information Processing Systems http://nips.cc/
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Roman Beliy, Guy Gaziv, Assaf Hoogi, Francesca Strappini, Tal Golan, Michal Irani
Developing amethod forhigh-quality reconstruction ofseenimages fromthecorresponding brain activity is an important milestone towards decoding the contents of dreams and mental imagery (Fig 1a). In this task, one attempts to solve for the mapping between fMRI recordings and their corresponding natural images, using many "labeled"{Image, fMRI} pairs (i.e., images and their corresponding fMRIresponses).
Unsupervised Learning of View-invariant Action Representations
Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli
Neural Information Processing Systems http://nips.cc/