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 &lt;wiki.pine(a)gmail.com&gt; wrote: image also classification where can _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/ wiki/Mailing_lists/Guidelines and https://meta.wikimedia.org/ wiki/Wikimedia-l New messages to: Wikimedia-l(a)lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, <mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe>

I had a plan to do a more thorough description on meta, but my plans are always to optimistic… ;) Question 1: I wonder if anyone has done any work on categorization of images on Commons by using neural nets. Question 2: I wonder if anyone has used Siamese networks to do more general categorization of images. A Siamese network is a kind of network used for face recognition. It is called Siamese network because it is two parallel networks in its simplest form, but one of the networks are precomputed so in effect you only evaluates one of the networks. Usually you have one face provided by a camera and run through one network, and a number of other precomputed faces for the other network. When the difference is small, then you have a face match. In general you can have any kind of image as long as you can train a kind of fingerprint type of vector. This fingerprint will then acts like locality-sensitive hashing [1]. Because you can create this LSH from the vector, you can store alternate models in database, so you can later search it and find usable models. Those models can then interpret the vector form of the fingerprint, and by evaluating those models with actual input (ie the fingerprint vector) you can get an estimate for how probable it is that the image belong to a specific category. What you would get from the previous process is a list of probable categorizations. That list can be sorted, and a user trying to categorize an image can then chose to select some of the categories. Note that the output of the network is not just a few categories, it can be a variant number of models that outputs a probability for a specific category. It is a bit like a map-reduce, where the you find (filter) the possible models and then evaluate (map) those models with the fingerprint vector. It is perhaps not obvious, but the idea behind Siamese networks are two parallel networks, but the implementation is with a single active network. Also the implemented network has a first part that computes the fingerprint vector (kind of a bottleneck network) and the second part are the stored models that takes this fingerprint vector and calculates a single output for the probability of a single category. [1] https://en.wikipedia.org/wiki/Locality-sensitive_hashing On Mon, Jan 22, 2018 at 6:47 AM, Pine W &lt;wiki.pine(a)gmail.com&gt; wrote: the neither fingerprints