Characterizing Missing Information in Deep Networks Using Backpropagated Gradients
Deep networks face challenges of ensuring their robustness against inputs that cannot be effectively represented by information learned from training data. We attribute this vulnerability to the limitations inherent to activation-based representation. To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information. In addition, we propose a directional constraint on the gradients as an objective during training to improve the characterization of missing information. To validate the effectiveness of the proposed approach, we compare the anomaly detection performance of gradient-based and activation-based representations. We show that the gradient-based representation outperforms the activation-based representation by 0.093 in CIFAR-10 and 0.361 in CURE-TSR datasets in terms of AUROC averaged over all classes. Also, we propose an anomaly detection algorithm that uses the gradient-based representation, denoted as GradCon, and validate its performance on three benchmarking datasets. The proposed method outperforms the majority of the state-of-the-art algorithms in CIFAR-10, MNIST, and fMNIST datasets with an average AUROC of 0.664, 0.973, and 0.934, respectively.
The authors present an interesting idea: creating representations based on gradients with respect to the weights to supplement information missing from the training dataset. The idea if very well motivated and gets the reader excited. Their explanation is clear from a high-level, however, the paper as a whole lacks rigorous justification. A lot of space is spent reviewing the role of gradients in training, what reconstruction loss is, and the basics of back-propagation --- however, many of their own propositions (e.g. why constrain gradients?) are given without reasoning. The paper could be made more clear by giving details and by changing the flow. For instance, Section 4 first gives a high-level explanation, then begins discussing specific experiments, and then returns to the method and provides some details. Additionally, it isn*t until page 6 (Section 5) that the authors introduce GradCon, one of the more promising ideas shared, this should be a focal point in the paper and introduced early (and given more than two sentences for explanation). The main proposal appears to be a modification of the loss function, but the paper may benefit from discussing implementation details (for example, during training vs. testing). Finally, the Figures (2 and 3 in particular) are not clear and need more explanation given in the captions. Figure 3a and 3b tell an interesting story, but they*re not easily digestable, nor is the key take-away clear to the reader just by looking at the figure and caption. The experimental results look reasonable and thorough, however the methods are sold on the idea of better representations for data missing from the training set, whereas the results are focused on anomaly detection. The method looks particularly promising at anomaly detection tasks --- the authors may have a more clear paper if they focus on this aspect. Overall, the paper presents a promising idea but it needs a more clear and rigorous presentation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Characterizing Missing Information in Deep Networks Using Backpropagated Gradients
Deep networks face challenges of ensuring their robustness against inputs that cannot be effectively represented by information learned from training data. We attribute this vulnerability to the limitations inherent to activation-based representation. To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information. In addition, we propose a directional constraint on the gradients as an objective during training to improve the characterization of missing information. To validate the effectiveness of the proposed approach, we compare the anomaly detection performance of gradient-based and activation-based representations. We show that the gradient-based representation outperforms the activation-based representation by 0.093 in CIFAR-10 and 0.361 in CURE-TSR datasets in terms of AUROC averaged over all classes. Also, we propose an anomaly detection algorithm that uses the gradient-based representation, denoted as GradCon, and validate its performance on three benchmarking datasets. The proposed method outperforms the majority of the state-of-the-art algorithms in CIFAR-10, MNIST, and fMNIST datasets with an average AUROC of 0.664, 0.973, and 0.934, respectively.
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 authors present an interesting idea: creating representations based on gradients with respect to the weights to supplement information missing from the training dataset. The idea if very well motivated and gets the reader excited. Their explanation is clear from a high-level, however, the paper as a whole lacks rigorous justification. A lot of space is spent reviewing the role of gradients in training, what reconstruction loss is, and the basics of back-propagation --- however, many of their own propositions (e.g. why constrain gradients?) are given without reasoning. The paper could be made more clear by giving details and by changing the flow. For instance, Section 4 first gives a high-level explanation, then begins discussing specific experiments, and then returns to the method and provides some details. Additionally, it isn*t until page 6 (Section 5) that the authors introduce GradCon, one of the more promising ideas shared, this should be a focal point in the paper and introduced early (and given more than two sentences for explanation). The main proposal appears to be a modification of the loss function, but the paper may benefit from discussing implementation details (for example, during training vs. testing). Finally, the Figures (2 and 3 in particular) are not clear and need more explanation given in the captions. Figure 3a and 3b tell an interesting story, but they*re not easily digestable, nor is the key take-away clear to the reader just by looking at the figure and caption. The experimental results look reasonable and thorough, however the methods are sold on the idea of better representations for data missing from the training set, whereas the results are focused on anomaly detection. The method looks particularly promising at anomaly detection tasks --- the authors may have a more clear paper if they focus on this aspect. Overall, the paper presents a promising idea but it needs a more clear and rigorous presentation.
assistant
The authors present an interesting idea: creating representations based on gradients with respect to the weights to supplement information missing from the training dataset. The idea if very well motivated and gets the reader excited. Their explanation is clear from a high-level, however, the paper as a whole lacks rigorous justification. A lot of space is spent reviewing the role of gradients in training, what reconstruction loss is, and the basics of back-propagation --- however, many of their own propositions (e.g. why constrain gradients?) are given without reasoning. The paper could be made more clear by giving details and by changing the flow. For instance, Section 4 first gives a high-level explanation, then begins discussing specific experiments, and then returns to the method and provides some details. Additionally, it isn*t until page 6 (Section 5) that the authors introduce GradCon, one of the more promising ideas shared, this should be a focal point in the paper and introduced early (and given more than two sentences for explanation). The main proposal appears to be a modification of the loss function, but the paper may benefit from discussing implementation details (for example, during training vs. testing). Finally, the Figures (2 and 3 in particular) are not clear and need more explanation given in the captions. Figure 3a and 3b tell an interesting story, but they*re not easily digestable, nor is the key take-away clear to the reader just by looking at the figure and caption. The experimental results look reasonable and thorough, however the methods are sold on the idea of better representations for data missing from the training set, whereas the results are focused on anomaly detection. The method looks particularly promising at anomaly detection tasks --- the authors may have a more clear paper if they focus on this aspect. Overall, the paper presents a promising idea but it needs a more clear and rigorous presentation.