  Continual acquisition of novel experience without interfering previously
learned knowledge, i.e. continual learning, is critical for artificial neural
networks, but limited by catastrophic forgetting. A neural network adjusts its
parameters when learning a new task, but then fails to conduct the old tasks
well. By contrast, the brain has a powerful ability to continually learn new
experience without catastrophic interference. The underlying neural mechanisms
possibly attribute to the interplay of hippocampus-dependent memory system and
neocortex-dependent memory system, mediated by prefrontal cortex. Specifically,
the two memory systems develop specialized mechanisms to consolidate
information as more specific forms and more generalized forms, respectively,
and complement the two forms of information in the interplay. Inspired by such
brain strategy, we propose a novel approach named triple memory networks (TMNs)
for continual learning. TMNs model the interplay of hippocampus, prefrontal
cortex and sensory cortex (a neocortex region) as a triple-network architecture
of generative adversarial networks (GAN). The input information is encoded as
specific representation of the data distributions in a generator, or
generalized knowledge of solving tasks in a discriminator and a classifier,
with implementing appropriate brain-inspired algorithms to alleviate
catastrophic forgetting in each module. Particularly, the generator replays
generated data of the learned tasks to the discriminator and the classifier,
both of which are implemented with a weight consolidation regularizer to
complement the lost information in generation process. TMNs achieve new
state-of-the-art performance on a variety of class-incremental learning
benchmarks on MNIST, SVHN, CIFAR-10 and ImageNet-50, comparing with strong
baseline methods.
