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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 17·5 min read
A Systematic Comparison of Training Objectives for Out-of-Distribution Detection in Image Classification
TL;DR
A systematic comparison of four training objectives for out-of-distribution (OOD) detection in image classification reveals that Cross-Entropy Loss consistently outperforms others in OOD scenarios. Prototype Loss and Average Precision Loss show competitive results in specific contexts.
✦ Why It Matters
Choose Cross-Entropy Loss for robust OOD detection in your image classification projects today.
Key Takeaways
- Use Cross-Entropy Loss for consistent OOD detection performance.
- Consider Prototype Loss for specific OOD scenarios.
- Evaluate Average Precision Loss in ranking-based tasks.
- Implement standardized OpenOOD protocols for reliable comparisons.
Full Summary
Out-of-distribution (OOD) detection is essential for ensuring safety in applications like autonomous driving and medical imaging. This study evaluates four training objectives: Cross-Entropy Loss, Prototype Loss, Triplet Loss, and Average Precision (AP) Loss, each representing different approaches to supervision in machine learning.
Using a standardized OpenOOD protocol with the ResNet-18 model, the researchers assessed how these objectives influence OOD detection performance. Results indicate that while Cross-Entropy Loss achieves the highest overall area under the receiver operating characteristic curve (AUROC) for both near- and far-OOD scenarios, Prototype Loss and AP Loss can be effective under certain conditions.
The findings highlight the importance of selecting appropriate training objectives to enhance OOD detection in image classification tasks.