Real World Scenario

Research on real-world scenarios focuses on bridging the gap between idealized models and practical applications across diverse fields. Current efforts concentrate on improving model robustness and generalization by addressing data limitations through techniques like counterfactual explanations and data augmentation, and employing architectures such as LLMs, CNNs, and ViTs, along with advanced algorithms like reinforcement learning and ensemble methods. This work is crucial for advancing the reliability and trustworthiness of AI systems in safety-critical applications like autonomous driving and healthcare, as well as improving the accuracy and efficiency of tools in areas such as search engines and biometric identification.