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First of all, congratulations on this post, or even on the whole of distill.pub. I really think it excels at explaining a lot of things precisely, beautifully, and thoroughly!
In terms of CNN, I like looking at the receptive field a lot. In this post, you mention that the first layers do have a smaller RF. I would love to be able to visualize that, either by cropping the visualizations, or by overlaying a small ROI box. (Personally, I also like to shade everything except the RF of a neuron by blending it to black 80% or so.)
I do understand the limitations of this approach, and the difference between the theoretical and the effective receptive field (which would also be nice to be visualized), but I also do think that a simple RF visualization would be a good addition to this post.
The text was updated successfully, but these errors were encountered:
Hi Hans! I totally agree that explicitly visualizing the receptive field is a valuable thing one could add. You can see it implicitly in a neuron feature visualization, but you could explore it more explicitly.
That said, I think it's outside the scope of this particular article.
First of all, congratulations on this post, or even on the whole of distill.pub. I really think it excels at explaining a lot of things precisely, beautifully, and thoroughly!
In terms of CNN, I like looking at the receptive field a lot. In this post, you mention that the first layers do have a smaller RF. I would love to be able to visualize that, either by cropping the visualizations, or by overlaying a small ROI box. (Personally, I also like to shade everything except the RF of a neuron by blending it to black 80% or so.)
I do understand the limitations of this approach, and the difference between the theoretical and the effective receptive field (which would also be nice to be visualized), but I also do think that a simple RF visualization would be a good addition to this post.
The text was updated successfully, but these errors were encountered: