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Feature learning

Known as: Learning representation, Representation learning, Unsupervised feature learning 
In machine learning, feature learning or representation learning is a set of techniques that learn a feature: a transformation of raw data input to a… 
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories… 
Highly Cited
2017
Highly Cited
2017
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent… 
Highly Cited
2016
Highly Cited
2016
We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto… 
Highly Cited
2016
Highly Cited
2016
Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-of-the… 
Highly Cited
2013
Highly Cited
2013
Transfer learning is established as an effective technology in computer vision for leveraging rich labeled data in the source… 
Highly Cited
2011
Highly Cited
2011
A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made… 
Highly Cited
2011
Highly Cited
2011
Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging… 
Highly Cited
2007
Highly Cited
2007
Abstract We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of… 
Highly Cited
2007
Highly Cited
2007
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more… 
Highly Cited
2006
Highly Cited
2006
We present a method for learning a low-dimensional representation which is shared across a set of multiple related tasks. The…