Non Exemplar Based Class Incremental Learning

Non-exemplar-based Class Incremental Learning is a machine learning method that focuses on continuously learning new classes without retaining samples of old classes, to achieve knowledge updating and expansion of the model. This approach aims to address the issue of catastrophic forgetting in class incremental learning by optimizing learning strategies and adjusting the model structure, ensuring that the acquisition of new knowledge does not significantly impair the performance of previously learned knowledge. In the field of computer vision, this method has significant application value for tasks such as image classification and object detection in dynamic environments, effectively enhancing the adaptability and robustness of the model.