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Anonymous review 2 #4
This is an anonymous review that I am sharing from a peer reviewer. They sent it to me as an e-mail with formatted text. Since I don't know of a way to copy-paste a formatted e-mail into Markdown, I'm just sharing it as an RTF document:
Transcription of review from the RTF document, to preserve record of review in GitHub issues.
Thoughts on first read-through:
Thank you for your high-quality feedback! We went through every bullet point and have made numerous changes to the article based upon the review you provided. These can collectively be found in the pull request #7.
We are especially grateful both for the insightful critique as well as the hints about sections that may be hard to understand—we do not just want to be factually correct, but also approachable.
We use GoogLeNet trained on ImageNet.
We added additional captioning on the hero diagram mentioning both the model and the dataset it was trained on in aa394e7.
Since we know of no quantiative metric that could settle this yet, and this is a subjective judgement, we changed the wording to "high-quality".
We have added that we are mostly using the channel objective.
We mean the latter when we say facets and have added additional clarification for the term in the Diversity section.
We have explicitly labelled those images as "Simple Optimization" and "Optimization with diversity" to make the difference clearer.
We have added a footnote with the mathematical definition of our diversity term: cosine dissimilarity between the flattened Gram matrices.
We have added a section to the introduction of the optimization section stating that naive optimization doesn't work; linking to the section about challenegs in feature visualization by optimization.
We have added a section explicitly enumerating areas of future work we believe to be important.
Thank you again for your time and helpful comments! We think the article was significantly improved by incorporating your feedback. :-)