Unsupervised learning with sparse space-and-time autoencoders

Convolutional networks were initially developed for supervised learning. They are used in deep learning to classify two-dimensional spatial information such as hand writing samples and photographs [16]. In the one dimensional setting, they have been applied to temporal data such as audio recordings of speech and music, and writing encoded at either the character level or the word level. In the three dimensional setting, applications have included medical scans, object detection for self driving cars, and object recognition from RGB-D photos. Videos, with their two spatial dimensions and one time dimension can also be seen as 2 1 3 dimensional objects for purposes of applying convolutional networks [29]. The movement of 3D objects happens in 3 1 4 dimensional space-time, but 4D ConvNets are relatively unexplored.