Domain Transferability

Domain transferability focuses on adapting machine learning models trained on one dataset (source domain) to perform well on a different, but related, dataset (target domain). Current research emphasizes improving transferability across diverse domains and model architectures, particularly in natural language processing and computer vision, often employing techniques like adversarial training, knowledge distillation, and careful selection of transferable features based on statistical divergence measures. This research is crucial for building more robust and efficient AI systems that can generalize beyond the limited scope of their initial training data, impacting applications ranging from conversational AI to object detection and abuse detection on social media.