Difference between revisions of "DSC Capstone2020"
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* [https://github.com/jacobgil/pytorch-grad-cam Grad-CAM implementation in Pytorch] | * [https://github.com/jacobgil/pytorch-grad-cam Grad-CAM implementation in Pytorch] | ||
* [https://cocodataset.org COCO Image Dataset] | * [https://cocodataset.org COCO Image Dataset] | ||
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| + | * What CNN layer outputs look like in Tensorflow: | ||
| + | ** https://github.com/conan7882/CNN-Visualization/tree/master/doc/firstfilter#visualization-of-filters-and-feature-maps-of-googlenet | ||
| + | ** https://medium.com/@awjuliani/visualizing-neural-network-layer-activation-tensorflow-tutorial-d45f8bf7bbc4 | ||
| + | |||
| + | * Paper on visualzing CNNs with deconvolution: https://arxiv.org/pdf/1311.2901.pdf | ||
| + | * Various ways to visualize diverse information in CNNs: https://github.com/conan7882/CNN-Visualization | ||
| + | * Interesting visualization: http://people.csail.mit.edu/torralba/research/drawCNN/drawNet.html | ||
| + | * More stuff: | ||
| + | ** https://towardsdatascience.com/how-to-visualize-convolutional-features-in-40-lines-of-code-70b7d87b0030 | ||
| + | ** https://towardsdatascience.com/understanding-your-convolution-network-with-visualizations-a4883441533b | ||
| + | ** http://cs231n.github.io/understanding-cnn/ | ||
| + | ** http://cs-people.bu.edu/jmzhang/excitationbp.html | ||
Revision as of 21:59, 6 October 2020
Contents |
DSC 180 Capstone Domain: Explainable AI (Section A01)
- Instructor: Jurgen Schulze
- Discussion: Wednesdays 12-12:50pm on Zoom at https://ucsd.zoom.us/j/9100475160
- Piazza Discussion Board: https://piazza.com/ucsd/fall2020/dsc180
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.
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 | Literature review | |
| 3 | Oct 21 | 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
- Learning Deep Features for Discriminative Localization
- Grad-CAM: Why did you say that?Visual Explanations from Deep Networks via Gradient-based Localization
Useful Links
- Main DSC Capstone Website
- Introduction to how CNNs Work
- Tensorflow Playground
- Grad-CAM implementation in Pytorch
- COCO Image Dataset
- What CNN layer outputs look like in Tensorflow:
- Paper on visualzing CNNs with deconvolution: https://arxiv.org/pdf/1311.2901.pdf
- Various ways to visualize diverse information in CNNs: https://github.com/conan7882/CNN-Visualization
- Interesting visualization: http://people.csail.mit.edu/torralba/research/drawCNN/drawNet.html
- More stuff:
