SEIS: Subspace-based Equivariance and Invariance Scores for Neural Representations
arXiv:2602.04054v2 Announce Type: replace
Abstract: Understanding how neural representations respond to geometric transformations is essential for evaluating whether learned features preserve meaningful spatial structure. Existing approaches primarily…