ASYMMETRIC COLLABORATIVE NETWORK: TRANSFERABLE LIFELONG LEARNING FOR REMOTE SENSING IMAGES

Lifelong learning is important in remote sensing image understanding, especially in an open world where streaming of data are incrementally available. Current related research mainly focuses on preserving learned knowledge (i.e., avoiding catastrophic forgetting) while devoting less attention to the exploitation of historical knowledge to facilitate the learning of new knowledge. Here, we propose a new framework to bridge this gap. It consists of two sub-networks that memorize old and learn new knowledge separately, and exploit the synergy of transfer cells and triple distillation to take advantage of the valuable knowledge from previous learned tasks to facilitate learning new tasks while avoiding forgetting old tasks. Furthermore, it uses an asymmetric structure considering feature generality on historical tasks and scale- and channel-feature dependence on remote sensing images for specific tasks. Experimental results obtained in scene classification on several open benchmarks demonstrate the effectiveness of the framework.