Negative Flip

"Negative flip" refers to the phenomenon where a more accurate model makes incorrect predictions on samples correctly classified by a less accurate model, hindering progress in various machine learning domains. Current research focuses on mitigating negative flips through techniques like ensemble methods, data augmentation (e.g., image flipping), and architectural constraints in neural network design, aiming to improve model robustness and consistency across different platforms. Addressing negative flips is crucial for enhancing the reliability and generalizability of machine learning models, impacting applications ranging from image classification and natural language processing to federated learning and click-through rate prediction.