Unsupervised Domain Expansion

Unsupervised Domain Expansion is a technique that can expand a model's knowledge to new domains without labeled data, aiming to enhance the model's generalization ability on unseen data. By automatically identifying and learning features of new domains, this method can effectively strengthen the model's adaptability and robustness, thereby providing more stable and reliable performance in diverse real-world application scenarios. Its core value lies in reducing the cost of manual annotation, accelerating cross-domain model applications, and promoting the widespread adoption and deep integration of artificial intelligence technologies.