  Biological vision systems are unparalleled in their ability to learn visual
representations without supervision. In machine learning, self-supervised
learning (SSL) has led to major advances in forming object representations in
an unsupervised fashion. Such systems learn representations invariant to
augmentation operations over images, like cropping or flipping. In contrast,
biological vision systems exploit the temporal structure of the visual
experience during natural interactions with objects. This gives access to
"augmentations" not commonly used in SSL, like watching the same object from
multiple viewpoints or against different backgrounds. Here, we systematically
investigate and compare the potential benefits of such time-based augmentations
during natural interactions for learning object categories. Our results show
that time-based augmentations achieve large performance gains over
state-of-the-art image augmentations. Specifically, our analyses reveal that:
1) 3-D object manipulations drastically improve the learning of object
categories; 2) viewing objects against changing backgrounds is important for
learning to discard background-related information from the latent
representation. Overall, we conclude that time-based augmentations during
natural interactions with objects can substantially improve self-supervised
learning, narrowing the gap between artificial and biological vision systems.
