The present invention proposes a simple, effective, and novel solution called neuron-level plasticity control (NPC) to resolve the issue of catastrophic forgetting in an artificial neural network. The proposed method preserves existing knowledge by controlling the plasticity of a network at a neural level instead of a connection level while learning a new task. The neuron-level plasticity control allows each neuron to evaluate the importance and apply a low learning rate to integrate important neurons. An extension of NPC called scheduled NPC (SNPC) is also proposed. The extension uses learning schedule information to clearly protect important neurons. Experimental results for incremental MNIST (iMNIST) and incremental CIFAR100 data sets show that NPC and SNPC are much more effective than the connection level integration approach method. Specifically, SNPC shows superior performance in the two data sets.
- Method for overcoming catastrophic forgetting by neuron-level plasticity control and computing system performing the same