New research compares training objectives for AI out-of-distribution detection

A new research paper systematically compares four training objectives for out-of-distribution (OOD) detection in image classification. The study evaluated Cross-Entropy Loss, Prototype Loss, Triplet Loss, and Average Precision (AP) Loss using the OpenOOD protocols and a ResNet-18 model. Results indicate that while Cross-Entropy, Prototype, and AP Loss achieve similar in-distribution accuracy, Cross-Entropy Loss generally provides the most consistent OOD detection performance. AI

IMPACT Provides insights into optimizing AI model robustness for safety-critical applications by comparing different training methodologies.

RANK_REASON Research paper published on arXiv detailing a systematic comparison of training objectives for OOD detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Average Precision (AP) Loss
• Furkan Genç
• Cross-Entropy Loss
• OpenOOD
• Prototype Loss
• ResNet-18
• Triplet Loss

• paper
• safety

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