Accuracy on the Line

"Accuracy-on-the-line," the observed positive correlation between a model's in-distribution and out-of-distribution accuracy, is a central theme in current machine learning research, with a focus on understanding its limitations and developing robust alternatives. Recent work investigates scenarios where this relationship breaks down, particularly in the presence of noisy data, spurious features, and distribution shifts, leading to unreliable model selection and generalization. Researchers are exploring improved evaluation metrics, novel model architectures (like Spiking Neural Networks and binary neural networks), and advanced algorithms (including active learning techniques) to enhance model accuracy and robustness across diverse data conditions. These efforts aim to improve the reliability and trustworthiness of machine learning models in real-world applications.