Unsupervised or Indirectly Supervised Learning
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).
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Unsupervised Learning of View-invariant Action Representations
Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli
Neural Information Processing Systems http://nips.cc/
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PacGAN: The power of two samples in generative adversarial networks
Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh
Neural Information Processing Systems http://nips.cc/
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MarginGAN: Adversarial Training in Semi-Supervised Learning
The newfeature isthattheclassifier attempts toincrease themarginofrealexamples and to decrease the margin of fake examples.
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Learning from Label Proportions with Generative Adversarial Networks
Jiabin Liu, Bo Wang, Zhiquan Qi, YingJie Tian, Yong Shi
Neural Information Processing Systems http://nips.cc/
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Graph Agreement Models for Semi-Supervised Learning
Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios, Sujith Ravi, Andrew Tomkins
Neural Information Processing Systems http://nips.cc/
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