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Contrastive Learning and the Emergence of Attributes Associations
Contrastive learning is a semi-supervised learning scheme based on the application of identity preserving transformations on the object input representations.
A Simple Generative Network
Generative neural networks are able to mimic intricate probability distributions such as those of handwritten text, natural images, etc.
Unsupervisedly Learned Representations: Should the Quest be Over?
There exists a Classification accuracy gap of about 20% between our best methods of generating Unsupervisedly Learned Representations and the accuracy rates achieved by (naturally Unsupervisedly Learning) humans.
An Unsupervised Learning Classifier with Competitive Error Performance
An unsupervised learning classification model is described.
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