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KISSME was a promising metric learning approach that got lost in the deep learning flood. This is a shame since it had very good results at the time, but it was hindered by the quirks of the PCA pre-processing stage. In a nutshell, this paper reimagines this approach using deep convnets, gets good results on modern benchmark datasets and does not require meticulous preprocessing tricks. It therefore deserves to be published, since it rescues a promising approach from obscurity and does a competent job of illustrating its potential in a modern context, with all the necessary discussion of related work on old (LMNN, MMC, PCCA) and new (deep, triplet-loss based) metric learning approaches.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
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user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



KISSME was a promising metric learning approach that got lost in the deep learning flood. This is a shame since it had very good results at the time, but it was hindered by the quirks of the PCA pre-processing stage. In a nutshell, this paper reimagines this approach using deep convnets, gets good results on modern benchmark datasets and does not require meticulous preprocessing tricks. It therefore deserves to be published, since it rescues a promising approach from obscurity and does a competent job of illustrating its potential in a modern context, with all the necessary discussion of related work on old (LMNN, MMC, PCCA) and new (deep, triplet-loss based) metric learning approaches.
assistant
KISSME was a promising metric learning approach that got lost in the deep learning flood. This is a shame since it had very good results at the time, but it was hindered by the quirks of the PCA pre-processing stage. In a nutshell, this paper reimagines this approach using deep convnets, gets good results on modern benchmark datasets and does not require meticulous preprocessing tricks. It therefore deserves to be published, since it rescues a promising approach from obscurity and does a competent job of illustrating its potential in a modern context, with all the necessary discussion of related work on old (LMNN, MMC, PCCA) and new (deep, triplet-loss based) metric learning approaches.