An Online Recurring Concept Meta-learning For Evolving Streams

Humans learning involves remembering patterns from the past to better understand recurring concepts as their knowledge grows. However, previous knowledge in deep neural networks could gradually forget when they are trained on a new concept. In this paper, we address this problem by learning a general representation that can be able to remember the previous information and promote the future learning. In this pursuit, a new controller is introduced by the meta-learning strategy that guides the network to keep balance between the previously learned concepts and the new concept, hence avoids catastrophic forgetting. In Online Recurring Concept Meta-Learning (ORCML), we propose a bi-level learning strategy, emphasizing the hidden representation learning of different concept drift in model-level learning, and obtaining a set of shared parameters through the global meta-learning strategy. Through extensive experiments, we demonstrate that our proposed framework has significant improvements over the state-of-art methods.