  Humans can continuously learn new knowledge. However, machine learning models
suffer from drastic dropping in performance on previous tasks after learning
new tasks. Cognitive science points out that the competition of similar
knowledge is an important cause of forgetting. In this paper, we design a
paradigm for lifelong learning based on meta-learning and associative mechanism
of the brain. It tackles the problem from two aspects: extracting knowledge and
memorizing knowledge. First, we disrupt the sample's background distribution
through a background attack, which strengthens the model to extract the key
features of each task. Second, according to the similarity between incremental
knowledge and base knowledge, we design an adaptive fusion of incremental
knowledge, which helps the model allocate capacity to the knowledge of
different difficulties. It is theoretically analyzed that the proposed learning
paradigm can make the models of different tasks converge to the same optimum.
The proposed method is validated on the MNIST, CIFAR100, CUB200 and ImageNet100
datasets.
