Exploring LOTS in Deep Neural Networks
Deep neural networks have recently demonstrated excellent performance on various tasks. Despite recent advances, our understanding of these learning models is still incomplete, at least, as their unexpected vulnerability to imperceptibly small, non-random perturbations revealed. The existence of these so-called adversarial examples presents a serious problem of the application of vulnerable machine learning models. In this paper, we introduce the layerwise origin-target synthesis (LOTS) that can serve multiple purposes. First, we can use it as a visualization technique that gives us insights into the function of any intermediate feature layer by showing the notion of a particular input in deep neural networks. Second, our approach can be applied to assess the invariance of the learned features captured at any layer with respect to the class of the particular input. Finally, we can also utilize LOTS as a general way of producing a vast amount of diverse adversarial examples that can be used for training to further improve the robustness of machine learning models and their performance as well.
The paper presents a new exciting layerwise origin-target synthesis method both for generating a large number of diverse adversarials as well as for understanding the robustness of various layers. The methodology is then used to visualize the amount of perturbation necessary for producing a change for higher level features. The approach to match the features of another unrelated image is interesting and it goes beyond producing adversarials for classification. It can also generate adversarials for face-recognition and other models where the result is matched with some instance from a database. Pro: The presented approach is definitely sound, interesting and original. Con: The analyses presented in this paper are relatively shallow and don*t touch the most obvious questions. There is not much experimental quantitative evidence for the efficacy of this method compared with other approaches to produce adversarials. The visualization is not very exciting and it is hard to any draw any meaningful conclusions from them. It would definitely improve the paper if it would present some interesting conclusions based on the new ideas.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Exploring LOTS in Deep Neural Networks
Deep neural networks have recently demonstrated excellent performance on various tasks. Despite recent advances, our understanding of these learning models is still incomplete, at least, as their unexpected vulnerability to imperceptibly small, non-random perturbations revealed. The existence of these so-called adversarial examples presents a serious problem of the application of vulnerable machine learning models. In this paper, we introduce the layerwise origin-target synthesis (LOTS) that can serve multiple purposes. First, we can use it as a visualization technique that gives us insights into the function of any intermediate feature layer by showing the notion of a particular input in deep neural networks. Second, our approach can be applied to assess the invariance of the learned features captured at any layer with respect to the class of the particular input. Finally, we can also utilize LOTS as a general way of producing a vast amount of diverse adversarial examples that can be used for training to further improve the robustness of machine learning models and their performance as well.
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>



The paper presents a new exciting layerwise origin-target synthesis method both for generating a large number of diverse adversarials as well as for understanding the robustness of various layers. The methodology is then used to visualize the amount of perturbation necessary for producing a change for higher level features. The approach to match the features of another unrelated image is interesting and it goes beyond producing adversarials for classification. It can also generate adversarials for face-recognition and other models where the result is matched with some instance from a database. Pro: The presented approach is definitely sound, interesting and original. Con: The analyses presented in this paper are relatively shallow and don*t touch the most obvious questions. There is not much experimental quantitative evidence for the efficacy of this method compared with other approaches to produce adversarials. The visualization is not very exciting and it is hard to any draw any meaningful conclusions from them. It would definitely improve the paper if it would present some interesting conclusions based on the new ideas.
assistant
The paper presents a new exciting layerwise origin-target synthesis method both for generating a large number of diverse adversarials as well as for understanding the robustness of various layers. The methodology is then used to visualize the amount of perturbation necessary for producing a change for higher level features. The approach to match the features of another unrelated image is interesting and it goes beyond producing adversarials for classification. It can also generate adversarials for face-recognition and other models where the result is matched with some instance from a database. Pro: The presented approach is definitely sound, interesting and original. Con: The analyses presented in this paper are relatively shallow and don*t touch the most obvious questions. There is not much experimental quantitative evidence for the efficacy of this method compared with other approaches to produce adversarials. The visualization is not very exciting and it is hard to any draw any meaningful conclusions from them. It would definitely improve the paper if it would present some interesting conclusions based on the new ideas.