Difference between revisions of "DSC Capstone2020"
From Immersive Visualization Lab Wiki
| Line 1: | Line 1: | ||
=DSC 180 Capstone Domain: Explainable AI (Section A01)= | =DSC 180 Capstone Domain: Explainable AI (Section A01)= | ||
| − | |||
| − | |||
* Weekly Discussion: Wednesdays 12-12:50pm on Zoom at https://ucsd.zoom.us/j/9100475160 | * Weekly Discussion: Wednesdays 12-12:50pm on Zoom at https://ucsd.zoom.us/j/9100475160 | ||
| Line 11: | Line 9: | ||
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]. | ||
| + | |||
| + | ==Papers== | ||
| + | |||
| + | * [https://arxiv.org/pdf/1512.04150.pdf Learning Deep Features for Discriminative Localization] | ||
| + | * [https://arxiv.org/pdf/1610.02391v1.pdf Grad-CAM: Why did you say that?Visual Explanations from Deep Networks via Gradient-based Localization] | ||
| + | |||
| + | ==Useful Links== | ||
| + | |||
| + | * [https://medium.com/datadriveninvestor/introduction-to-how-cnns-work-77e0e4cde99b Introduction to how CNNs Work] | ||
| + | * [http://playground.tensorflow.org Tensorflow Playground] | ||
| + | * [https://github.com/jacobgil/pytorch-grad-cam Grad-CAM implementation in Pytorch] | ||
Revision as of 09:18, 2 October 2020
Contents |
DSC 180 Capstone Domain: Explainable AI (Section A01)
- Weekly Discussion: Wednesdays 12-12:50pm on Zoom at https://ucsd.zoom.us/j/9100475160
- Discussion board: Piazza
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.
Papers
- Learning Deep Features for Discriminative Localization
- Grad-CAM: Why did you say that?Visual Explanations from Deep Networks via Gradient-based Localization
