  Self-paced learning has been beneficial for tasks where some initial
knowledge is available, such as weakly supervised learning and domain
adaptation, to select and order the training sample sequence, from easy to
complex. However its applicability remains unexplored in unsupervised learning,
whereby the knowledge of the task matures during training. We propose a novel
HYperbolic Self-Paced model (HYSP) for learning skeleton-based action
representations. HYSP adopts self-supervision: it uses data augmentations to
generate two views of the same sample, and it learns by matching one (named
online) to the other (the target). We propose to use hyperbolic uncertainty to
determine the algorithmic learning pace, under the assumption that less
uncertain samples should be more strongly driving the training, with a larger
weight and pace. Hyperbolic uncertainty is a by-product of the adopted
hyperbolic neural networks, it matures during training and it comes with no
extra cost, compared to the established Euclidean SSL framework counterparts.
When tested on three established skeleton-based action recognition datasets,
HYSP outperforms the state-of-the-art on PKU-MMD I, as well as on 2 out of 3
downstream tasks on NTU-60 and NTU-120. Additionally, HYSP only uses positive
pairs and bypasses therefore the complex and computationally-demanding mining
procedures required for the negatives in contrastive techniques. Code is
available at https://github.com/paolomandica/HYSP.
