Domain Discrepancy

Domain discrepancy, the difference in data distributions between training and testing datasets, hinders the generalization ability of machine learning models. Current research focuses on mitigating this discrepancy through various techniques, including adversarial training, contrastive learning, and knowledge distillation, often implemented within deep neural network architectures like YOLO and transformers. These methods aim to improve model performance in cross-domain scenarios, such as adapting models trained on simulated data to real-world applications or transferring knowledge between different datasets in fields like medical image analysis and natural language processing. Overcoming domain discrepancy is crucial for building robust and reliable AI systems applicable across diverse real-world settings.