Why there is a very little difference when evaluating on the non-rotational input and the rotational input?
Our formula are equivariant, but in our implementation we discretized the formula which causes this little difference.
The Spherical CNN takes lots of memory. Now we plan to build a larger model and train on multiple gpus. Is this possible?
This is due to the 3 dimensional internal representations. Last attempt to support multi-gpu was done here #8 You can also look at other architecture that consume less memory like 1902.04615