RSA tools

We provide some basic functions to conduct a similarity analysis on the extracted features. Note that these provide only a basic functionality and are not optimized for speed. For more advanced and optimized analysis tools, we recommend to use the rsatoolbox library.

Representational Similarity Analysis (RSA)

Compare representational (dis-)similarity matrices (RDMs) corresponding to model features and human representations (e.g., fMRI recordings).

from thingsvision.core.rsa import compute_rdm, correlate_rdms

rdm_dnn = compute_rdm(features, method='correlation')
corr_coeff = correlate_rdms(rdm_dnn, rdm_human, correlation='pearson')

Centered Kernel Alignment (CKA)

Perform CKA to compare image features of two different model architectures for the same layer, or two different layers of the same architecture.

from thingsvision.core.cka import CKA

m = # number of images (e.g., features_i.shape[0])
kernel = 'linear'
cka = CKA(m=m, kernel=kernel)
rho = cka.compare(X=features_i, Y=features_j)