Practice Metrics For Robustness (13.6.2) - Privacy-Aware and Robust Machine Learning
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Metrics for Robustness

Practice - Metrics for Robustness

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does 'accuracy under adversarial perturbation' measure?

💡 Hint: Think about how a model performs under attack.

Question 2 Easy

How do robust accuracy and clean accuracy differ?

💡 Hint: Consider which inputs are being used for evaluations.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is accuracy under adversarial perturbation?

Accuracy on clean data
Accuracy on adversarial data
Overall accuracy

💡 Hint: Consider what happens during an attack.

Question 2

True or False: Robust accuracy is measured on clean inputs.

True
False

💡 Hint: Think about the definitions.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

A model has a robust accuracy of 60% and a clean accuracy of 95%. Evaluate the implications of this disparity in a real-world application.

💡 Hint: Think about trust and safety.

Challenge 2 Hard

Design an experiment to assess the L_2 norm bounds for a given dataset. Explain the steps involved.

💡 Hint: What mathematical tools will you need?

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.