OpenHAIV: A Framework Towards Practical Open-World Learning

Continual learning aims to enable models to retain existing knowledge while continuously acquiring new knowledge, typically derived from new data, under the constraint of limited or restricted access to data related to previously learned knowledge. Depending on the defined scenarios, continual learning is typically categorized into task-incremental learning, class-incremental learning [4], and domain-incremental learning. Among these three settings, class-incremental learning has been the most extensively studied. Taking classification tasks as an example, class-incremental learning divides a dataset into multiple sessions, where the classes in different sessions do not overlap. The model is required to learn the classes of each session over time and is evaluated on all classes after each update. In contrast, task-incremental learning assumes knowledge of which session the data belongs to during evaluation, allowing the model to classify only within the corresponding task. On the other hand, in domain-incremental learning, different sessions involve data from the same set of classes but with different distributions. The model is required to correctly identify the domain of the test data while performing classification. Research targeting these settings has led to significant improvements in model performance in scenarios requiring multi-stage fine-tuning.