Larger memory consumption and slower training speed

[quva-lab]

Larger memory consumption and slower training speed

[Gabri95]

Hi @EBGU

Are you referring to this model? e2wrn.py

This is the case if fixparams=True.
Because equivariance induces a stronger weight sharing, an equivariant model has usually less parameters than an equivalent conventional model.
For this reason, it is common to compare to a scaled up version of the equivariant model which has the same number of parameters of the conventional model. This results in a wider model (roughly by a factor of sqrt(N), where N is the group size).

You can build a non-scaled up model by setting fixparams=False.
This results in a model with more or less the same size of the conventional architecture.

Note that in our paper we compare the conventional model with both equivariant models above (fixparams=False and fixparams=True). The README reports a summary of the results here.

I hope this answers your question

Best,
Gabriele Cesa