Guest Editorial: Deep Learning
Abstract
Deep Learning methods aim at learning feature hierarchies.
Applications of deep learning to vision tasks date back to convolutional
networks in the early 1990s. These methods have
been the subject of a recent surge of interest for two main
reasons: when labeled data is scarce, unsupervised learning
algorithms can learn useful feature hierarchies. When
labeled data is abundant, supervised methods can be used
to train very large networks on very large datasets through
the use of high-performance computers. Such large networks
have been shown to outperform previous state-of-theart
methods on several perceptual tasks, including categorylevel
object recognition, object detection and semantic
segmentation.
Applications of deep learning to vision tasks date back to convolutional
networks in the early 1990s. These methods have
been the subject of a recent surge of interest for two main
reasons: when labeled data is scarce, unsupervised learning
algorithms can learn useful feature hierarchies. When
labeled data is abundant, supervised methods can be used
to train very large networks on very large datasets through
the use of high-performance computers. Such large networks
have been shown to outperform previous state-of-theart
methods on several perceptual tasks, including categorylevel
object recognition, object detection and semantic
segmentation.
Research Areas
