Transfer of View-manifold Learning to Similarity Perception of Novel Objects
We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience. The re-training process effectively performs distance metric learning under the object persistency constraints, to modify the view-manifold of object representations. It reduces the effective distance between the representations of different views of the same object without compromising the distance between those of the views of different objects, resulting in the untangling of the view-manifolds between individual objects within the same category and across categories. This untangling enables the model to discriminate and recognize objects within the same category, independent of viewpoints. We found that this ability is not limited to the trained objects, but transfers to novel objects in both trained and untrained categories, as well as to a variety of completely novel artificial synthetic objects. This transfer in learning suggests the modification of distance metrics in view- manifolds is more general and abstract, likely at the levels of parts, and independent of the specific objects or categories experienced during training. Interestingly, the resulting transformation of feature representation in the deep networks is found to significantly better match human perceptual similarity judgment than AlexNet, suggesting that object persistence could be an important constraint in the development of perceptual similarity judgment in biological neural networks.
On one hand this paper is fairly standard in that it uses deep metric learning with a Siamese architecture. On the other, the connections to human perception involving persistence is quite interesting. I*m not an expert in human vision, but the comparison in general and the induced hierarchical groupings in particular seem like something that should interest people in this community. The experimental suite is ok but I was disappointed that it is 100% synthetic. The authors could have used a minimally viable real dataset such as ALOI http://aloi.science.uva.nl . In summary, the mechanics of the proposed approach are not new, but the findings about the transfer of similarity judgement to novel object classes are interesting.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Transfer of View-manifold Learning to Similarity Perception of Novel Objects
We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience. The re-training process effectively performs distance metric learning under the object persistency constraints, to modify the view-manifold of object representations. It reduces the effective distance between the representations of different views of the same object without compromising the distance between those of the views of different objects, resulting in the untangling of the view-manifolds between individual objects within the same category and across categories. This untangling enables the model to discriminate and recognize objects within the same category, independent of viewpoints. We found that this ability is not limited to the trained objects, but transfers to novel objects in both trained and untrained categories, as well as to a variety of completely novel artificial synthetic objects. This transfer in learning suggests the modification of distance metrics in view- manifolds is more general and abstract, likely at the levels of parts, and independent of the specific objects or categories experienced during training. Interestingly, the resulting transformation of feature representation in the deep networks is found to significantly better match human perceptual similarity judgment than AlexNet, suggesting that object persistence could be an important constraint in the development of perceptual similarity judgment in biological neural networks.
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>



On one hand this paper is fairly standard in that it uses deep metric learning with a Siamese architecture. On the other, the connections to human perception involving persistence is quite interesting. I*m not an expert in human vision, but the comparison in general and the induced hierarchical groupings in particular seem like something that should interest people in this community. The experimental suite is ok but I was disappointed that it is 100% synthetic. The authors could have used a minimally viable real dataset such as ALOI http://aloi.science.uva.nl . In summary, the mechanics of the proposed approach are not new, but the findings about the transfer of similarity judgement to novel object classes are interesting.
assistant
On one hand this paper is fairly standard in that it uses deep metric learning with a Siamese architecture. On the other, the connections to human perception involving persistence is quite interesting. I*m not an expert in human vision, but the comparison in general and the induced hierarchical groupings in particular seem like something that should interest people in this community. The experimental suite is ok but I was disappointed that it is 100% synthetic. The authors could have used a minimally viable real dataset such as ALOI http://aloi.science.uva.nl . In summary, the mechanics of the proposed approach are not new, but the findings about the transfer of similarity judgement to novel object classes are interesting.