Abstract：To solve the catastrophic forgetting problem caused by incremental learning, a deep incremental image classification method based on double-branch iteration is proposed. The primary network is utilized to store the acquired old class knowledge, while the branch network is exploited to learn the new class knowledge. The parameters of the branch network are optimized by the weight of the primary network in the incremental iteration process. Density peak clustering method is employed to select typical samples from the iterative dataset and construct retention set. The retention set is added into the incremental iteration training to mitigate catastrophic forgetting. The experiments demonstrate the better performance of the proposed method.
何丽, 韩克平, 朱泓西, 刘颖. 双分支迭代的深度增量图像分类方法[J]. 模式识别与人工智能, 2020, 33(2): 150-159.
HE Li, HAN Keping, ZHU Hongxi, LIU Ying. Deep Incremental Image Classification Method Based on Double-Branch Iteration. , 2020, 33(2): 150-159.
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2020, Vol. 33 
