Answers.Body: <p>These are the tools that help you create a set of &quot;regression models&quot; that are trained to predict outcomes. <strong>Regression activation maps</strong> are used to visualize which parts of an image are most important for a given machine-learning model (regression model). By overlaying the maps on top of an image, one can see which pixels are most likely to influence the model's output. This can be useful for debugging models, or for understanding how a model makes predictions.</p>
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  <p>For <strong>example</strong>, if you have a regression model that predicts the price of a house based on its features, the activation map would show which features of the house are most important for the model.</p>
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Answers.CreationDate: 2022-10-18T01:05:05.850Z
Answers.Text: Do you know who is the "inventor" of RAM? Especially, what is the first paper that coined this term and described this concept in details?
Answers.UserDisplayName: rtnF14
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-10-16T21:38:52.470Z
Answers.LastActivityDate: 2022-10-16T21:38:52.470Z
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Body: <p>I stumbled across a research topic : &quot;the training of multiple regression activation maps (RAM) to study the interaction between two images&quot;. But, I cant find any decent definition of &quot;regression activation maps&quot; anywhere.</p>
  
Tags: <image-processing>
Title: What is regression activation maps?
UserDisplayName: rtnF14
ThreadText: # What is regression activation maps?
  
  (Asked by: username_0 on 2022-10-14)
  
  I stumbled across a research topic : "the training of multiple regression activation maps (RAM) to study the interaction between two images". But, I cant find any decent definition of "regression activation maps" anywhere.
  
  **Tags:** image-processing
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-10-16. Score: 0 | Accepted answer
  
  These are the tools that help you create a set of "regression models" that are trained to predict outcomes. **Regression activation maps** are used to visualize which parts of an image are most important for a given machine-learning model (regression model). By overlaying the maps on top of an image, one can see which pixels are most likely to influence the model's output. This can be useful for debugging models, or for understanding how a model makes predictions.
  
  > For **example**, if you have a regression model that predicts the price of a house based on its features, the activation map would show which features of the house are most important for the model.
  
  > **Comments:**
  > **username_0**: Do you know who is the "inventor" of RAM? Especially, what is the first paper that coined this term and described this concept in details?  
  
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