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ViCCo-Group / frrsa

Python package to conduct feature-reweighted representational similarity analysis.
https://www.sciencedirect.com/science/article/pii/S105381192200413X
GNU Affero General Public License v3.0
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Apply betas to new images #47

Open PhilippKaniuth opened 1 year ago

PhilippKaniuth commented 1 year ago

Motivated from https://github.com/ViCCo-Group/frrsa/issues/43, if the aim is to generalize the betas to another imageset:

The user submits two (or several) targets and corresponding two (or several) predictors. One these target-predictor pairs will be used to fit the statistical model as usual.

Then, the betas will be extracted and used to reweight another predictor. This predictor must come from the same system, e.g. if using a DNN, it must be the same DNN-module. However, this new predictor can be for different images. Using the betas, this new predictor is reweighted. Finally, the reweighted second predictor is evaluated with its corresponding target.

The question this would answer is: if I reweight my predictor with regards to target, how well does the fitted statistical model (i.e. the betas of the regularized regression) generalize if applied to another predictor with different images?

In contrast to #46, this does allow generalizing the statistical model to a matrix with (dis-)similarities from different images.

PhilippKaniuth commented 1 year ago

This is actively worked on on the branch generalize-betas.