3.1 Role of Data-Augmentation
Many SSL methods, especially joint embedding methods derived from Chen et al. [2020b],
require a way to deﬁne positive views from a given image to learn invariances. The proxy
used in these SSL methods is to leverage data augmentation to deﬁne these invariances.
For example, by using diﬀerent crops of a given images and positive view, the SSL model
will be trained to produce a representation that is invariant to these diﬀerent crops. When
using a grayscale operation, or a colorjitter one as positive views, the representation will
have to be invariant to the color information. Thus, the deep nature of what is learned
by the SSL models is deﬁned by the data augmentation pipeline. It is worth noting that
perfect invariance is not achieved thanks to the projector [Bordes et al., 2022a], which
helps improve performance on tasks which are not entirely invariant. Chen et al. [2020b]
study how much inﬂuence have speciﬁc data augmentations on SimCLR with respect
A Cookbook of Self-Supervised Learning