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[R] Reimplementation of Hyperspherical Prototype Networks (NeurIPS 2019)

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!

submitted by /u/ACTBRUH
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.