This chapter covers results in the paper **Attribution-Based Confidence Metric For Deep Neural Networks ** available at this author's link 2019 version.

The lecture slides are available here.

---
primaryColor: steelblue
shuffleQuestions: false
shuffleAnswers: true
---
### The ABC metric is based on
- [ ] the theory of counterfactuals.
- [x] the System 1 - System 2 theory.
> Correct.
- [ ] has no theoretical basis.
### The ABC metric relies mainly on
1. [x] attributions.
> Correct.
1. [ ] softmax values of neural networks.
1. [ ] neural network logits.
1. [ ] inputs to the neural network.

---
primaryColor: steelblue
shuffleQuestions: false
shuffleAnswers: true
---
### ABC metric samples the:
- [ ] randomly in the input space.
- [x] attribution-neighborhood of the input space.
> Correct.
- [ ] weights of the neural network.

---
primaryColor: steelblue
shuffleQuestions: false
shuffleAnswers: true
---
### In this paper, the ABC metric has been applied to
- [ ] images and natural language inputs.
- [x] images only.
> Correct.
- [ ] natural language inputs only.

There are no programming assignments for this lecture.