Fig. 1.
Longitudinal Self-Supervised Learning (LSSL) aims to learn representations from observed images, which are assumed to be generated from a set of hidden factors. In this example, the variation of the repeated measures of two subjects (blue and red, t encodes the order of visits) is assumed to relate to an increase in brain age. LSSL then disentangles a 1D direction τ linked to brain age from the representation space such that the developmental trajectories of subject-specific representations zt are colinear with τ.