Feature Forgetting

Feature forgetting, the phenomenon where neural networks lose previously learned features during subsequent training, is a significant challenge in continual and transfer learning. Current research focuses on mitigating this issue through techniques like feature erasure and transfer mechanisms, often implemented within encoder-decoder architectures or by employing attention-based methods to selectively retain or discard information. Addressing feature forgetting is crucial for improving the efficiency and robustness of machine learning models across various applications, including image recognition, natural language processing, and person re-identification, by enabling more effective knowledge accumulation and preventing performance degradation on previously learned tasks.