Domain Gap

Domain gap refers to the performance degradation of machine learning models when applied to data from a different distribution than that used for training. Current research focuses on bridging this gap using various techniques, including domain adaptation methods (e.g., adversarial training, contrastive learning), and leveraging model architectures like transformers and diffusion models to learn more robust and generalizable representations. Addressing domain gap is crucial for improving the reliability and applicability of machine learning across diverse real-world scenarios, impacting fields ranging from medical image analysis and autonomous driving to remote sensing and natural language processing.