Efficient Saliency Maps for Explainable AI
We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular gradient methods. It is also quantitatively similar or better in accuracy. Our technique works by measuring information at the end of each network scale. This is then combined into a single saliency map. We describe how saliency measures can be made more efficient by exploiting Saliency Map Order Equivalence. Finally, we visualize individual scale/layer contributions by using a Layer Ordered Visualization of Information. This provides an interesting comparison of scale information contributions within the network not provided by other saliency map methods. Our method is generally straight forward and should be applicable to the most commonly used CNNs. (Full source code is available at http://www.anonymous.submission.com).
This paper presents a method for creating saliency maps reflecting what a network deems important while also proposing an interesting method for visualizing this. The central premise for the method of characterizing relative importance of information represented by the network is based on an information theoretic measure. A variety of results are presented to examine the impact of keeping or removing information deemed important by this measure and a comparison is made to existing approaches as a justification for the proposed methods. I find that the proposed method appears to be theoretically sound and is interesting in revealing differences to other information theoretic methods especially in the early layers. The relative similarity in later layers is also an interesting observation and one that is relatively hard to characterize. Perhaps the strongest case for the proposed method comes from the keep accuracy subject to SMOE scale. The removal accuracy is a bit less convincing but appears to be a fair and honest evaluation. Moreover, the LOVI scheme for visualization is interesting in itself and I found this aspect of the work to be thought provoking with respect to how such methods are examined from a qualitative perspective.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Efficient Saliency Maps for Explainable AI
We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular gradient methods. It is also quantitatively similar or better in accuracy. Our technique works by measuring information at the end of each network scale. This is then combined into a single saliency map. We describe how saliency measures can be made more efficient by exploiting Saliency Map Order Equivalence. Finally, we visualize individual scale/layer contributions by using a Layer Ordered Visualization of Information. This provides an interesting comparison of scale information contributions within the network not provided by other saliency map methods. Our method is generally straight forward and should be applicable to the most commonly used CNNs. (Full source code is available at http://www.anonymous.submission.com).
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>



This paper presents a method for creating saliency maps reflecting what a network deems important while also proposing an interesting method for visualizing this. The central premise for the method of characterizing relative importance of information represented by the network is based on an information theoretic measure. A variety of results are presented to examine the impact of keeping or removing information deemed important by this measure and a comparison is made to existing approaches as a justification for the proposed methods. I find that the proposed method appears to be theoretically sound and is interesting in revealing differences to other information theoretic methods especially in the early layers. The relative similarity in later layers is also an interesting observation and one that is relatively hard to characterize. Perhaps the strongest case for the proposed method comes from the keep accuracy subject to SMOE scale. The removal accuracy is a bit less convincing but appears to be a fair and honest evaluation. Moreover, the LOVI scheme for visualization is interesting in itself and I found this aspect of the work to be thought provoking with respect to how such methods are examined from a qualitative perspective.
assistant
This paper presents a method for creating saliency maps reflecting what a network deems important while also proposing an interesting method for visualizing this. The central premise for the method of characterizing relative importance of information represented by the network is based on an information theoretic measure. A variety of results are presented to examine the impact of keeping or removing information deemed important by this measure and a comparison is made to existing approaches as a justification for the proposed methods. I find that the proposed method appears to be theoretically sound and is interesting in revealing differences to other information theoretic methods especially in the early layers. The relative similarity in later layers is also an interesting observation and one that is relatively hard to characterize. Perhaps the strongest case for the proposed method comes from the keep accuracy subject to SMOE scale. The removal accuracy is a bit less convincing but appears to be a fair and honest evaluation. Moreover, the LOVI scheme for visualization is interesting in itself and I found this aspect of the work to be thought provoking with respect to how such methods are examined from a qualitative perspective.