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A plain well-trained deep learning model often does not have the ability tolearn new knowledge without forgetting the previously learned knowledge, whichis known as catastrophic forgetting. Here we propose a novel method,SupportNet, to efficiently and effectively solve the catastrophic forgettingproblem in the class incremental learning scenario. SupportNet combines thestrength of deep learning and support vector machine (SVM), where SVM is usedto identify the support data from the old data, which are fed to the deeplearning model together with the new data for further training so that themodel can review the essential information of the old data when learning thenew information. Two powerful consolidation regularizers are applied tostabilize the learned representation and ensure the robustness of the learnedmodel. We validate our method with comprehensive experiments on various tasks,which show that SupportNet drastically outperforms the state-of-the-artincremental learning methods and even reaches similar performance as the deeplearning model trained from scratch on both old and new data. Our program isaccessible at: https://github.com/lykaust15/SupportNet

SupportNet: solving catastrophic forgetting in class incremental learning with support data