Hierarchical Prototypes Polynomial Softmax Loss Function for Visual Classification
Round 1
Reviewer 1 Report
In this paper, authors presents a hierarchical prototype loss function by adding loss functions to different layers of the deep neural network. They improved the performance of the semantic feature extraction at the bottom of the network and they calculated loss method with a polynomial function, which is a kernel method that can effectively improve linear separability of the low-dimensional space. Through various experiments using multiple public datasets, it was proven that the proposed method is effective.
The paper is well written and very interested for the reader.
Author Response
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Author Response File: Author Response.docx
Reviewer 2 Report
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Comments for author File: Comments.pdf
Author Response
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Round 2
Reviewer 2 Report
The authors have included all my comments and suggestions successfully. We recommend for publication in Applied Sciences.