Distribution Detection Benchmark

Distribution detection benchmarks evaluate the ability of machine learning models to identify data points originating from distributions unseen during training (out-of-distribution or OOD detection). Current research focuses on improving OOD detection in various contexts, including class-incremental learning and zero-shot scenarios, often employing vision-language models, energy-based models, and contrastive learning techniques. These benchmarks are crucial for assessing the robustness and reliability of machine learning systems, particularly in safety-critical applications where encountering unexpected data is a significant concern. The development of improved benchmarks and detection methods is driving progress towards more trustworthy and dependable AI systems.