Reviews: Trading robust representations for sample complexity through self-supervised visual experience

This submission describes a model for unsupervised feature learning that is based on the idea that an image and the set of its transformations (described here as an orbit of the image under the action of a transformation group) should have similar representations according to some loss function L. Two losses are considered. One is based on a ranking loss which enforces examples from the same orbit to be closer than those of different orbits. The other one is a reconstruction loss that enforces that an all examples from an orbit should be mapped to a canonical element of the orbit using an autoencoder-like function. Two broad classes of transformation groups are considered, the first one is a set of parametrized image transformations (as proposed by Dosovitskiy et al. 2016) and the other one is based on some prior knowledge from the data - in this case the tracking of faces in a video (as proposed by Wang et al. 2015). The proposed approach is described with a very clear framework, with proper definitions.