DSC Capstone2020

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DSC 180A Capstone Section A01: Explainable AI


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 learn about saliency and attention maps, then get to know a large publicly available image data set (COCO) and finally we are going to implement the Grad-CAM algorithm in PyTorch and apply it to the COCO image data set.

The quarter will end with a proposal for your capstone project, which you will be working on in the winter quarter.

This class will be entirely remote.

You can use the DSMLP cluster, which already has the COCO data set installed.


Week Discussion Date Discussion Wed 12-12:50pm (Link to Slides) Participation Tasks (Due Tuesdays at noon) Checkpoints
1 Oct 7 Overview None for week 1
2 Oct 14 Saliency Maps Literature review (due Oct 13)
3 Oct 21 COCO Image Data Set COCO Dataset (due Oct 20)
4 Oct 28 Introduction to Grad-CAM Custom COCO Demo (due Oct 27) Replication Checkpoint #1 (due Oct 30)
5 Nov 4 Deep Learning Introduction to Grad-CAM (due Nov 3)
6 Nov 11 Veterans Day (No Discussion) Deep Learning (due Nov 10)
7 Nov 18 Grad-CAM in Context PyTorch (due Nov 17) Replication Checkpoint #2 (due Nov 22)
8 Nov 25 Grad-CAM for XAI Grad-CAM in Context (due Nov 24)
9 Dec 2 Grad-CAM and Q2 Project Grad-CAM for XAI (due Dec 1)
10 Dec 9 Q2 Project Elevator Pitches Grad-CAM Evolution (due Dec 8) Final Replication Report (due Dec 11)

Relevant Publications

Useful Links

Direct CNN Visualization