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mvasil / fashion-compatibility

Learning Type-Aware Embeddings for Fashion Compatibility
BSD 3-Clause "New" or "Revised" License
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Maryland semantic category #20

Open tianyu-su opened 3 years ago

tianyu-su commented 3 years ago

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?

Thanks

Originally posted by @tianyu-su in https://github.com/mvasil/fashion-compatibility/issues/2#issuecomment-720886549

BryanPlummer commented 3 years ago

This was addressed in #2