<p>Yes you are correct that GradCam does not support regression in its formulation, but most gradient-based saliency map methods do support regression, for example:</p>
<ol>
<li>Guided Backpropagation</li>
<li>Integrated Gradients</li>
<li>SmoothGrad</li>
</ol>
<p>Also in non-gradient methods, LIME supports regression as well.</p>

CC BY-SA 4.0
2022-12-24T01:47:49.207Z
Thanks for you answering! Whether these methods are also useful in relatively simple Fully connected neural network or is only  used for CNNs. And how can i choose a suitable one among them? Are there a document about limitations or comparison about these methods.
minghuisvn
CC BY-SA 4.0
2022-12-21T11:11:48.257Z
2022-12-21T11:11:48.257Z
Dr. Snoopy
<p>I mainly tackle regression problems by CNNs, and want to find a reliable method to calculate the heatmaps for NN's results. However, I find almost all interpretation methods including CAM is used for classification NNs but not for regression NNs.</p>
<p>Is there any interpretation method suitable for CNNs which do regression tasks?</p>

<convolutional-neural-networks><regression><algorithm-request><model-request><explainable-ai>
Is there any interpretation method suitable for CNNs which do regression tasks?
minghuisvn
# Is there any interpretation method suitable for CNNs which do regression tasks?

(Asked by: username_0 on 2022-12-21)

I mainly tackle regression problems by CNNs, and want to find a reliable method to calculate the heatmaps for NN's results. However, I find almost all interpretation methods including CAM is used for classification NNs but not for regression NNs.

Is there any interpretation method suitable for CNNs which do regression tasks?

**Tags:** convolutional-neural-networks, regression, algorithm-request, model-request, explainable-ai


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## 1 answers

### Answer by username_1 on 2022-12-21. Score: 1

Yes you are correct that GradCam does not support regression in its formulation, but most gradient-based saliency map methods do support regression, for example:

1. Guided Backpropagation
2. Integrated Gradients
3. SmoothGrad

Also in non-gradient methods, LIME supports regression as well.

> **Comments:**
> **username_0**: Thanks for you answering! Whether these methods are also useful in relatively simple Fully connected neural network or is only  used for CNNs. And how can i choose a suitable one among them? Are there a document about limitations or comparison about these methods.  

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