Difference between revisions of "DSC Capstone2020"

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(Papers)
(Papers)
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* Paper for replication: [https://arxiv.org/pdf/1610.02391v1.pdf Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization]
 
* Paper for replication: [https://arxiv.org/pdf/1610.02391v1.pdf Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization]
 
* Paper on COCO image dataset: [https://arxiv.org/abs/1405.0312 Microsoft COCO: Common Objects in Context]
 
* Paper on COCO image dataset: [https://arxiv.org/abs/1405.0312 Microsoft COCO: Common Objects in Context]
* Original attention map paper: [https://arxiv.org/pdf/1312.6034.pdf Deep Inside Convolutional Networks: VisualisingImage Classification Models and Saliency Maps]
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* Original attention map paper: [https://arxiv.org/pdf/1312.6034.pdf Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps]
 
* [https://arxiv.org/pdf/1512.04150.pdf Learning Deep Features for Discriminative Localization]
 
* [https://arxiv.org/pdf/1512.04150.pdf Learning Deep Features for Discriminative Localization]
 
* [https://arxiv.org/pdf/1311.2901.pdf Visualizing CNNs with deconvolution]
 
* [https://arxiv.org/pdf/1311.2901.pdf Visualizing CNNs with deconvolution]

Revision as of 00:26, 7 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 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.

Schedule

Week Date Discussion Topic Replication Tasks
1 Oct 7 Overview None for week 1
2 Oct 14 Attention Maps Literature review
3 Oct 21 Image Data Set Description of data
4 Oct 28 Checkpoint #1 due
5 Nov 4 Description of methods
6 Nov 11 Veterans Day (No Discussion) Implementation of methods
7 Nov 18 Checkpoint #2 due
8 Nov 25 Implementation of Grad-CAM
9 Dec 2 Implementation of Grad-CAM
10 Dec 9 Final Report due

Papers

Useful Links

Direct CNN Visualization