However this speciﬁc combination of data augmentation was speciﬁcally designed to
reach good performances on ImageNet. Bordes et al. [2023a] study the impact of diﬀerent
choice of data augmentation on diﬀerent downstream tasks and found that even if the
addition of ColorJitter seem beneﬁcial for many classiﬁcation task it might not always
be the case on other downstream tasks. Similarly, Ericsson et al. [2021a] show that
diﬀerent augmentations lead to learning diﬀerent type of invariances for which some of
them are better on some downstream tasks than other. The authors suggest to merge
representations learned with diﬀerent augmentations to improve transferability across
a wider range of downstream task. There is also an hidden cost when using a complex
pipeline of data augmentation: the data preprocessing time which might slow down
signiﬁcantly the training. Thus, when the training budget matter, it might be preferable
A Cookbook of Self-Supervised Learning