OOD detection: training-time regularization

Estimated time: 0 minutes

Overview

Questions

• What are the key considerations when designing algorithms for OOD detection?
• How can OOD detection be incorporated into the loss functions of models?
• What are the challenges and best practices for training models with OOD detection capabilities?

Objectives

• Understand the critical design considerations for creating effective OOD detection algorithms.
• Learn how to integrate OOD detection into the loss functions of machine learning models.
• Identify the challenges in training models with OOD detection and explore best practices to overcome these challenges.

Training-time regularization for OOD detection