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).
I Summary The authors present a method that computes a saliency map after each scale block of a CNN and combines them according to the weights of the prior layers in a final saliency map. The paper gives two main contributions: SMOE, which captures the informativeness of the corresponding layers of the scale block and LOVI, a heatmap based on HSV, giving information of the region of interest according to the corresponding scale blocks. The method is interesting as it does not require multiple backward passes such as other gradient-based method and thus prove to be more time-efficient. II Comments 1. Content Overall the paper is very interesting, but it is not always clear what the contributions are.The authors refer to *scale* in CNNs, this could be described a little as to explain what are scale blocks in a CNN, before introducing the terminology. The proposed method *LOVI* is promising and I like the use of HSV for the different components. However, it is hard to read, especially when alpha-blended with a grayscale version of the original image. - The introduction doesn*t clearly state what are the contributions of the method, an example of possible applications would be appreciated (the following about robotic for example but with more details) - 3.1 I really enjoy the fact that different datasets are used for their different properties (foreground, background, sparsity), this is a nice touch Figure 4 results are a little underwhelming, SMOE*s scores are very close to the other methods - 3.2 The authors refer to Smoothgrad squared method, it is indeed a good process to refine saliency maps, however why it is used could be detailed, just as the parameters chosen for its implementation. - 4. The authors claim their implementation is *approximately x times faster* but there is no quantitative proof of it, which seems to be one of the selling-point of the paper (or at least one of the best results) 2. Writing Those typos do not impact the review score, I hope it can help the authors to gain more clarity in their writing. - Abstract: *it is also quantitatively similar or better in accuracy* -> shouldn*t it be *and* instead of *or*? - Intro *a gradient saliency map by trying to back-propagate a signal from one end of the network and project it onto the image plane* not well articulated, *by trying to back-propagate* -> *by back-propagating* (the claims seems weak otherwise) *is running a fishing expedition post hoc* ;) *An XAI tool which is too expensive will slow down training* Computationally expensive? - 2.1 *The resemblance to conditional entropy should be apparent* -> is apparent *we might say it is* -> it is (don*t weaken your claims) *we simple apply* -> we simply - 3.1 *if the results appeared terrible* -> terrible is too strong/not adapted. What is a bad result and why? after eq 7 * the second method is in information* -> an information -3.2 *which locations are most salient correctly* -> word missing? - Figure 5: *Higher accuracy values are better results for it* -> yield better results The phrasing with*one* as in *if one would like to etc* is used a lot through the paper, it can be a little redundant at times. III Conclusion The paper is interesting, however, one of the major contributions seems to be the speed of the method but no quantitative results have been reported. I would really appreciate seeing some experiments over it. Overall the results of the obtained maps are not very convincing compared to existing methods. I believe the writing of the paper could be wrapped around the speed of the method and in which context it would be important (robotic, medical?). The conclusion and discussion are short and could be filled a little more (some captions could be shortened in order to give more space). Edit: The authors have answered most of my concerns and I am happy to re-evaluate my score to weak accept.
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>



I Summary The authors present a method that computes a saliency map after each scale block of a CNN and combines them according to the weights of the prior layers in a final saliency map. The paper gives two main contributions: SMOE, which captures the informativeness of the corresponding layers of the scale block and LOVI, a heatmap based on HSV, giving information of the region of interest according to the corresponding scale blocks. The method is interesting as it does not require multiple backward passes such as other gradient-based method and thus prove to be more time-efficient. II Comments 1. Content Overall the paper is very interesting, but it is not always clear what the contributions are.The authors refer to *scale* in CNNs, this could be described a little as to explain what are scale blocks in a CNN, before introducing the terminology. The proposed method *LOVI* is promising and I like the use of HSV for the different components. However, it is hard to read, especially when alpha-blended with a grayscale version of the original image. - The introduction doesn*t clearly state what are the contributions of the method, an example of possible applications would be appreciated (the following about robotic for example but with more details) - 3.1 I really enjoy the fact that different datasets are used for their different properties (foreground, background, sparsity), this is a nice touch Figure 4 results are a little underwhelming, SMOE*s scores are very close to the other methods - 3.2 The authors refer to Smoothgrad squared method, it is indeed a good process to refine saliency maps, however why it is used could be detailed, just as the parameters chosen for its implementation. - 4. The authors claim their implementation is *approximately x times faster* but there is no quantitative proof of it, which seems to be one of the selling-point of the paper (or at least one of the best results) 2. Writing Those typos do not impact the review score, I hope it can help the authors to gain more clarity in their writing. - Abstract: *it is also quantitatively similar or better in accuracy* -> shouldn*t it be *and* instead of *or*? - Intro *a gradient saliency map by trying to back-propagate a signal from one end of the network and project it onto the image plane* not well articulated, *by trying to back-propagate* -> *by back-propagating* (the claims seems weak otherwise) *is running a fishing expedition post hoc* ;) *An XAI tool which is too expensive will slow down training* Computationally expensive? - 2.1 *The resemblance to conditional entropy should be apparent* -> is apparent *we might say it is* -> it is (don*t weaken your claims) *we simple apply* -> we simply - 3.1 *if the results appeared terrible* -> terrible is too strong/not adapted. What is a bad result and why? after eq 7 * the second method is in information* -> an information -3.2 *which locations are most salient correctly* -> word missing? - Figure 5: *Higher accuracy values are better results for it* -> yield better results The phrasing with*one* as in *if one would like to etc* is used a lot through the paper, it can be a little redundant at times. III Conclusion The paper is interesting, however, one of the major contributions seems to be the speed of the method but no quantitative results have been reported. I would really appreciate seeing some experiments over it. Overall the results of the obtained maps are not very convincing compared to existing methods. I believe the writing of the paper could be wrapped around the speed of the method and in which context it would be important (robotic, medical?). The conclusion and discussion are short and could be filled a little more (some captions could be shortened in order to give more space). Edit: The authors have answered most of my concerns and I am happy to re-evaluate my score to weak accept.
assistant
I Summary The authors present a method that computes a saliency map after each scale block of a CNN and combines them according to the weights of the prior layers in a final saliency map. The paper gives two main contributions: SMOE, which captures the informativeness of the corresponding layers of the scale block and LOVI, a heatmap based on HSV, giving information of the region of interest according to the corresponding scale blocks. The method is interesting as it does not require multiple backward passes such as other gradient-based method and thus prove to be more time-efficient. II Comments 1. Content Overall the paper is very interesting, but it is not always clear what the contributions are.The authors refer to *scale* in CNNs, this could be described a little as to explain what are scale blocks in a CNN, before introducing the terminology. The proposed method *LOVI* is promising and I like the use of HSV for the different components. However, it is hard to read, especially when alpha-blended with a grayscale version of the original image. - The introduction doesn*t clearly state what are the contributions of the method, an example of possible applications would be appreciated (the following about robotic for example but with more details) - 3.1 I really enjoy the fact that different datasets are used for their different properties (foreground, background, sparsity), this is a nice touch Figure 4 results are a little underwhelming, SMOE*s scores are very close to the other methods - 3.2 The authors refer to Smoothgrad squared method, it is indeed a good process to refine saliency maps, however why it is used could be detailed, just as the parameters chosen for its implementation. - 4. The authors claim their implementation is *approximately x times faster* but there is no quantitative proof of it, which seems to be one of the selling-point of the paper (or at least one of the best results) 2. Writing Those typos do not impact the review score, I hope it can help the authors to gain more clarity in their writing. - Abstract: *it is also quantitatively similar or better in accuracy* -> shouldn*t it be *and* instead of *or*? - Intro *a gradient saliency map by trying to back-propagate a signal from one end of the network and project it onto the image plane* not well articulated, *by trying to back-propagate* -> *by back-propagating* (the claims seems weak otherwise) *is running a fishing expedition post hoc* ;) *An XAI tool which is too expensive will slow down training* Computationally expensive? - 2.1 *The resemblance to conditional entropy should be apparent* -> is apparent *we might say it is* -> it is (don*t weaken your claims) *we simple apply* -> we simply - 3.1 *if the results appeared terrible* -> terrible is too strong/not adapted. What is a bad result and why? after eq 7 * the second method is in information* -> an information -3.2 *which locations are most salient correctly* -> word missing? - Figure 5: *Higher accuracy values are better results for it* -> yield better results The phrasing with*one* as in *if one would like to etc* is used a lot through the paper, it can be a little redundant at times. III Conclusion The paper is interesting, however, one of the major contributions seems to be the speed of the method but no quantitative results have been reported. I would really appreciate seeing some experiments over it. Overall the results of the obtained maps are not very convincing compared to existing methods. I believe the writing of the paper could be wrapped around the speed of the method and in which context it would be important (robotic, medical?). The conclusion and discussion are short and could be filled a little more (some captions could be shortened in order to give more space). Edit: The authors have answered most of my concerns and I am happy to re-evaluate my score to weak accept.