Comparing Npair loss with Proxy loss by yoke-tsne

In last week, I do an experiment on comparing embedding result between Npair loss and Proxy-loss, for testing yoke-tsne.

Npair loss is a popular method which try to push point in different class away and pull point in same class close (like triplet loss) , while the proxy loss just assign a specific place for each category and just push all points in this category in this place. I expect to see this difference on embedding result by yoked tsne.

In this experiment, which is same to last two, CAR dataset is split into two part, and I just train our embedding on the first part (by Npair and Proxy loss) and visualize it.

This result is as following (left part is Npari loss and right part is Proxy loss):

Here is the original one:

Here is the yoked one:

The yoked figures shows some interesting thing about those two embedding method:

First, In Npair Loss result, there are always some points in different class in cluster while are not in Proxy Loss. Those points should be very similar to the cluster, and the reason why the Proxy loss doesn't have such points is that the proxy fixed all points in one class to same place, so those points was moved into their own cluster. Next step, I will find corresponding image for those points.

Second, they are more clusters mixed up in proxy loss, maybe its shows that proxy play a bed performance in embedding.

Third, the corresponding clusters is in some place and comparing to the original one, the local relationship doesn't change too much.