Domain Sample

Domain sample research focuses on improving the generalization and robustness of machine learning models by addressing the challenges posed by data distribution shifts between training and testing environments. Current research emphasizes techniques like active learning, self-training, and adversarial training, often incorporating models such as GANs, transformers, and various neural networks to handle domain adaptation and out-of-distribution detection. These advancements are crucial for improving the reliability and applicability of machine learning across diverse real-world scenarios, particularly in areas like medical image analysis, autonomous driving, and natural language processing.