Coarse types

[gcucurull]

Hello,

I see from your dataloader that you use the attribute coarse_type for each product, which types have you used for the Maryland Polyvore dataset? In their case each item has very fine-grained category, have you mapped these categories to a coarser set of categories? Or do you work with the fine-grained categories?

Thanks!

[DaPenggg]

it is the key-point! is there any people would provide it to public. as i classify the datasets to 4 category:top,bottom,feet,accessories, but i can't obtain the result as describe in paper.

[BryanPlummer]

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.

[tianyu-su]

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

[marzooq-unbxd]

I am trying to use the already trained model in order to run it on a bunch of random outfits outside polyvore

{'accessories',
 'all-body',
 'bags',
 'bottoms',
 'hats',
 'jewellery',
 'outerwear',
 'scarves',
 'shoes',
 'sunglasses',
 'tops'}

How is the number of categories 11 and the number of typespaces 66 nc2 would make it 55, right?