Class Incremental Learning Method Based on Dynamic Structure Extension and Feature enhancement

With the advancement and widespread adoption of deep learning models, there has been a growing interest in class incremental learning. This approach aims to continuously learn new classes while retaining the recognition and memory capabilities for previously learned classes within an open and dynamic environment. The primary focus of class incremental learning is on maintaining the ability to learn new classes while mitigating catastrophic forgetting, thus achieving a better balance between stability and adaptability. To address this challenge, we propose an innovative method for incremental class learning that leverages dynamically representations to facilitate more efficient incremental class learning, preserving previously acquired features while adapting to new ones and effectively reducing catastrophic forgetting. Furthermore, we introduce a feature augmentation mechanism to significantly enhance the model's classification performance when incorporating new classes. This approach ensures efficient learning of both old and new classes without compromising the effectiveness of previous models. We conducted extensive experiments on two classes incremental learning benchmarks, consistently demonstrating significant performance advantages over other methods.