Domain Incremental Continual Learning

Domain incremental continual learning (DICL) focuses on training machine learning models that can continuously learn from new data streams representing different domains without forgetting previously acquired knowledge. Current research emphasizes techniques like generative replay to avoid storing sensitive past data, prompt-based methods to guide pre-trained models towards new domains, and strategies that selectively fine-tune only the most "forgetful" network parameters to improve efficiency. DICL addresses the critical need for robust and adaptable AI systems in real-world applications where data is constantly evolving and privacy is paramount, impacting fields like medical image analysis and natural language processing.