Author Interview - VOS- Learning What You Don't Know by Virtual Outlier Synthesis

Author Interview - VOS- Learning What You Don't Know by Virtual Outlier Synthesis

Yannic Kilcher via YouTube Direct link

- What are the most important components of the method?

6 of 11

6 of 11

- What are the most important components of the method?

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Author Interview - VOS- Learning What You Don't Know by Virtual Outlier Synthesis

Automatically move to the next video in the Classroom when playback concludes

  1. 1 - Intro
  2. 2 - What was the motivation behind this paper?
  3. 3 - Why object detection?
  4. 4 - What's the connection to energy-based models?
  5. 5 - Is a Gaussian mixture model appropriate for high-dimensional data?
  6. 6 - What are the most important components of the method?
  7. 7 - What are the downstream effects of the regularizer?
  8. 8 - Are there severe trade-offs to outlier detection?
  9. 9 - Main experimental takeaways?
  10. 10 - Why do outlier detection in the last layer?
  11. 11 - What does it take to finish a research projects successfully?

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.