Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Radioactive Data - Tracing Through Training

Yannic Kilcher via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a comprehensive video explanation of the research paper "Radioactive data: tracing through training" in this 36-minute tutorial. Dive into the concept of marking datasets with hidden "radioactive" tags to detect their usage in training neural classifiers. Learn about the mechanics of neural classifiers, radioactive marking techniques, high-dimensional random vectors, backpropagation of fake features, and feature space realignment. Examine experimental results, including black-box testing, and gain insights into the implications for data privacy and model training. Understand how this method offers improved signal-to-noise ratio compared to data poisoning and backdoor approaches, with the ability to detect radioactive data usage even when only 1% of the training data is marked.

Syllabus

- Intro & Overview
- How Neural Classifiers Work
- Radioactive Marking via Adding Features
- Random Vectors in High-Dimensional Spaces
- Backpropagation of the Fake Features
- Re-Aligning Feature Spaces
- Experimental Results
- Black-Box Test
- Conclusion & My Thoughts

Taught by

Yannic Kilcher

Reviews

Start your review of Radioactive Data - Tracing Through Training

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.