et al., 2021, Gidaris et al., 2018, Scherr et al., 2022]. Encoding true equivariance to
augmentations (which requires a mapping between embedding) is an active line of work
with approaches such as EquiMod[Dangovski et al., 2021], SEN [Park et al., 2022], or
[Marchetti et al., 2022] which also aims at splitting the representations as class and pose.
This idea of splitting representations as invariant and equivariant was also explored in
SIE [Garrido et al., 2023] and using Lie group formalism in Ibrahim et al. [2022].
A Cookbook of Self-Supervised Learning