Using the fine-grained categories you wouldn't be able to get enough samples to learn a projection. Instead, we used the index of the item, which roughly corresponded to a type. While this doesn't provide the same strict typing scenario that the Polyvore Outfits dataset does, it provides further evidence (coupled with the random assignment experiments) that there is some redundancy there and a different embedding for each pairwise comparison isn't strictly necessary.
So, the results of Maryland Dataset in your paper, how do you map the fine-grained categories to semantic (coarse) type?
I find the number of id in categoires.csv is smaller than Maryland dataset. Do you ignore some samples when you test on this dataset?
So, the results of Maryland Dataset in your paper, how do you map the fine-grained categories to semantic (coarse) type? I find the number of id in
categoires.csv
is smaller than Maryland dataset. Do you ignore some samples when you test on this dataset?Thanks
Originally posted by @tianyu-su in https://github.com/mvasil/fashion-compatibility/issues/2#issuecomment-720886549