Hi John,
I am having a little trouble with understanding your email from January 15th. Could you perhaps state your question or point in a different way?
Pine https://meta.wikimedia.org/wiki/User:Pine https://en.wikipedia.org/wiki/User:CatherineMunro/Bright_Places
On Mon, Jan 15, 2018 at 5:55 AM, John Erling Blad jeblad@gmail.com wrote:
This is the same as the entry on the wishlist for 2016, but describes the actual method. https://meta.wikimedia.org/wiki/2016_Community_Wishlist_ Survey/Categories/Commons#Use_computer_vision_to_propose_categories
Both contrastive and triplet loss can be used while learning, but neither are described at Wikipedia.
On Sun, Jan 14, 2018 at 8:16 PM, Pine W wiki.pine@gmail.com wrote:
Hi John,
I have not heard of an initiative to use Siamese neural networks for
image
classifications on on Commons. You might make a suggestion on the AI, Research, and/or Commons mailing lists regarding this idea. You might
also
make a suggestion in IdeaLab https://meta.wikimedia.org/wiki/Grants:IdeaLab.
Pine https://meta.wikimedia.org/wiki/User:Pine https://en.wikipedia.org/wiki/User:CatherineMunro/Bright_Places
On Sun, Jan 14, 2018 at 3:46 AM, John Erling Blad jeblad@gmail.com wrote:
Has anyone tried to use a Siamese neural network for image
classification
at Commons? I don't know if it will be good enough to run in autonomous mode, but it will probably be a huge help for those that do manual classification.
Imagine a network providing a list of possible categories, and the user just ticks off usable categories.
A Siamese network can be learned by using a triplet loss function,
where
the anchor and the positive candidate comes from the same category, and
the
negative candidate comes from an other category but are otherwise close
to
the anchor.
Output from the network is like a fingerprint, and those fingerprints
can
be compared to other images with known fingerprints, or against a generalized fingerprint for a category.
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