Difference between revisions of "DSC Capstone2020"

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The example we are going to use in this class is object recognition in images. We are first going to go over the basics of CNNs, then learn about saliency and attention maps, and finally we are going to implement the [http://gradcam.cloudcv.org Grad-CAM algorithm] in [https://pytorch.org PyTorch] and apply it to the [https://cocodataset.org COCO image data set].
 
The example we are going to use in this class is object recognition in images. We are first going to go over the basics of CNNs, then learn about saliency and attention maps, and finally we are going to implement the [http://gradcam.cloudcv.org Grad-CAM algorithm] in [https://pytorch.org PyTorch] and apply it to the [https://cocodataset.org COCO image data set].
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==Schedule==
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| Oct 14
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==Papers==
 
==Papers==

Revision as of 09:22, 2 October 2020

Contents

DSC 180 Capstone Domain: Explainable AI (Section A01)

Overview

In this capstone domain we are going to study how we can make machine learning systems more user friendly by exploiting additional knowledge we can derive from the system and present it to the user. These types of systems are called Explainable AI.

The example we are going to use in this class is object recognition in images. We are first going to go over the basics of CNNs, then learn about saliency and attention maps, and finally we are going to implement the Grad-CAM algorithm in PyTorch and apply it to the COCO image data set.

Schedule

Week Date Discussion Topic
1 Oct 7
2 Oct 14
3 Oct 21
4 Oct 28
5 Nov 4
6 Nov 11
7 Nov 18
8 Nov 25
9 Dec 2
10 Dec 9


Papers

Useful Links

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