Pre Trained Classifier

Pre-trained classifiers, foundational models already trained on massive datasets, are being extensively researched to improve their performance and address limitations. Current work focuses on enhancing their robustness to out-of-distribution data, improving the reliability of their probability outputs, and mitigating biases in their predictions through techniques like data point selection and post-processing. These advancements are significant because they enable more reliable and equitable applications of pre-trained classifiers across diverse domains, ranging from image recognition and natural disaster response to fairer decision-making systems.