Abstract:Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phaseby-phase. An inherent challenge of CIL is the stabilityplasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge. However, none of the existing CIL models can achieve the optimal tradeoff in different data-receiving settingswhere typically the training-from-half (TFH) setting needs more stability, but the training-from-scratch (TFS) nee… Show more
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