The idea of SimpleShot is almost the same as that of ProtoNet

[LoveMiki]

ProtoNet also uses the mean of each support class as its center and then implement Euclidean distance to train the network. The difference between SimpleShot and ProtoNet is that SimpleShot just added normalization and centering to the feature vectors before computing the Euclidean distance, is that correct?

[danoneata]

I believe the models are slightly different:

• SimpleShot trains by using a predictive function of the form w_k f(x, θ) with w_k and θ being free parameters (see first equation in §2 from their paper);
• ProtoNet (in the Euclidean distance scenario) trains by using a predictive function of the form 2 c_k f(x, θ) - ‖c_k‖² where only θ is a free parameter, while c_k is constrained to the centroid for class k (see equation 8 in their paper); the centroid c_k also depends on θ (see equation 1 in their paper).

I assume that the different parametrisations might yield learn different values θ for the embedding function f.

(Caveat: I'm not an expert and I've just read the SimpleShot paper.)