From 56c691046bbc46fcfb02fe76af634ed757c1c220 Mon Sep 17 00:00:00 2001 From: chokevin8 Date: Wed, 8 Nov 2023 18:13:19 +0900 Subject: [PATCH] updated CAM blog --- _posts/2023-10-20-class-activation-maps.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/_posts/2023-10-20-class-activation-maps.md b/_posts/2023-10-20-class-activation-maps.md index 19bae7fcf6b5..817d45e507e0 100644 --- a/_posts/2023-10-20-class-activation-maps.md +++ b/_posts/2023-10-20-class-activation-maps.md @@ -259,12 +259,15 @@ my trained model is actually focusing on the right parts of the images and not c The left images are GradCAMs, and the right images are HiResCAMs Now let's analyze the images of each row. The first row are the CAMs for the background, and we can see that there isn't a big difference between the two, except that HiResCAM does show a more "accurate" depiction, as it is a faithful explanation after all. +The second row are the CAMs for the blood vessels, and we can see that HiResCAM also has a more "accurate" depiction while GradCAM shows activation scores for spots that +are not blood vessel-specific. Lastly, the third row are the CAMs for the oil glands, and this is where GradCAM is a bit misleading. GradCAM does highlight the oil glands +successfully, but when looking at HiResCAM, we can see that the model doesn't only look at oil glands. Therefore, with HiResCAM, I can see that the model +is also mostly looking at nearby ECM and hair follicle areas for segmenting oil glands, which is quite interesting. - - +The last example like above is the reason why we must continue to explore and try different types of CAMs, and also explore other options of explainable AI as well. +Hope this helps! --- - *Image credits to:* - [Image Classification CAM Diagram](http://cnnlocalization.csail.mit.edu/) - [HiResCAM Diagram](https://arxiv.org/pdf/2011.08891.pdf)