Domain Incremental Learning

Domain incremental learning (DIL) focuses on training machine learning models to continuously adapt to new data domains without forgetting previously learned knowledge, a significant challenge in many real-world applications. Current research emphasizes efficient algorithms that avoid storing past data, often leveraging pre-trained models and techniques like prompt learning, model merging, and prototype-based methods to achieve this goal. The ability to build robust, adaptable models is crucial for various fields, including robotics, healthcare, and autonomous driving, where data distributions inevitably change over time.