rather than computing similarity to class centroid, they computed mean intra-class similarity for their diversity measure. they also define a "CLIP-I" measure for comparing similarity to real images, but this makes more sense in the personalization context I think than in a context where it's non-trivial to figure out what the relevant images in the training data were. I guess we could maybe use CLIP-retrieval to find those?
rather than computing similarity to class centroid, they computed mean intra-class similarity for their diversity measure. they also define a "CLIP-I" measure for comparing similarity to real images, but this makes more sense in the personalization context I think than in a context where it's non-trivial to figure out what the relevant images in the training data were. I guess we could maybe use CLIP-retrieval to find those?