Reviews: Hyperspherical Prototype Networks

Strengths – The paper presents a novel and well-motivated approach that is crisply explained, with significant experimental results to back it up. Clean and well-documented code has been provided and the results should be easily reproducible. In particular, the data-independent optimization, ability to bake in priors, computational savings by trimming output dimensionality, improvements over (de-facto) softmax classification in the presence of class imbalance, and suitability for multitask learning without loss weighting are strong wins. The related section does well to clearly identify and present threads in prior work. Weaknesses / Questions – What is the performance of the multitask baseline (Table 5) with appropriate loss weighting?