  Current state-of-the-art classification and detection algorithms rely on
supervised training. In this work we study unsupervised feature learning in the
context of temporally coherent video data. We focus on feature learning from
unlabeled video data, using the assumption that adjacent video frames contain
semantically similar information. This assumption is exploited to train a
convolutional pooling auto-encoder regularized by slowness and sparsity. We
establish a connection between slow feature learning to metric learning and
show that the trained encoder can be used to define a more temporally and
semantically coherent metric.

Computer Vision and Pattern Recognition;Machine Learning