A simple, effective, and novel solution called neuron-level plasticity control (NPC) is proposed in order to solve the issue of catastrophic forgetting in an artificial neural network. The proposed method controls the plasticity of a network at a nerve level rather than at a connection level during training of a new task, thereby conserving existing knowledge. The neuron-level plasticity control evaluates the importance of each neuron and applies a low training speed to integrate important neurons. In addition, an extension of NPC, called scheduled NPC (SNPC), is proposed. This extension uses training schedule information to more clearly protect important neurons. Results of experiments on incremental MNIST (iMNIST) and incremental CIFAR100 data sets show that NPC and SNPC are remarkably effective in comparison to connection-level integrated access methods, and in particular, SNPC exhibits excellent performance for the two data sets.
METHOD FOR OVERCOMING CATASTROPHIC FORGETTING THROUGH NEURON-LEVEL PLASTICITY CONTROL, AND COMPUTING SYSTEM PERFORMING SAME