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.