Link to paper: https://arxiv.org/abs/1901.10514

Link to my reimplementation: https://github.com/Abhishaike/HyperProtoNetReproduce

This is a Pytorch reimplementation of the NeurIPS 2019 paper ‘Hyperspherical Prototype Networks’ in Pytorch. This paper proposes an extension to Prototype Networks, in which the prototypes are placed a priori with large margin separation, and remain unchanged during the training/testing process of the model. The paper suggests that this extension allows for more flexible classification, regression, and joint multi-task training of regression/classification, and with higher accuracy compared to typical Prototype Networks.

This repo includes reproduced benchmarks for most of their datasets. Largely the same accuracy/error, but quite off on CIFAR-100 (not ImageNet-200 though for some reason), so it’s possible this is an issue on my end.

I also found their use of SGD for prototype creation to be unusual, considering that, the way they phrased the prototype problem, it seems like a job more for a constrained optimization algorithm. Alongside the SGD implementation (which are used for the included benchmarks), I added in two other optimization algorithms, one unconstrained (BFGS) and one constrained (SLSQP). These didn’t seem to change the results much.

This is my first reimplementation of a paper, so any critiques would be great!