Learning Invariant Representations Of Planar Curves
We propose a metric learning framework for the construction of invariant geometric functions of planar curves for the Euclidean and Similarity group of transformations. We leverage on the representational power of convolutional neural networks to compute these geometric quantities. In comparison with axiomatic constructions, we show that the invariants approximated by the learning architectures have better numerical qualities such as robustness to noise, resiliency to sampling, as well as the ability to adapt to occlusion and partiality. Finally, we develop a novel multi-scale representation in a similarity metric learning paradigm.
Pros : - New representation with nice properties that are derived and compared with a mathematical baseline and background - A simple algorithm to obtain the representation Cons : - The paper sounds like an applied maths paper, but further analysis on the nature of the representation could be done, for instance, by understanding the nature of each layer, or at least, the first.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning Invariant Representations Of Planar Curves
We propose a metric learning framework for the construction of invariant geometric functions of planar curves for the Euclidean and Similarity group of transformations. We leverage on the representational power of convolutional neural networks to compute these geometric quantities. In comparison with axiomatic constructions, we show that the invariants approximated by the learning architectures have better numerical qualities such as robustness to noise, resiliency to sampling, as well as the ability to adapt to occlusion and partiality. Finally, we develop a novel multi-scale representation in a similarity metric learning paradigm.
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>



Pros : - New representation with nice properties that are derived and compared with a mathematical baseline and background - A simple algorithm to obtain the representation Cons : - The paper sounds like an applied maths paper, but further analysis on the nature of the representation could be done, for instance, by understanding the nature of each layer, or at least, the first.
assistant
Pros : - New representation with nice properties that are derived and compared with a mathematical baseline and background - A simple algorithm to obtain the representation Cons : - The paper sounds like an applied maths paper, but further analysis on the nature of the representation could be done, for instance, by understanding the nature of each layer, or at least, the first.