Continual Learning of Context-dependent Processing in Neural Networks
This addresses the challenge of applying neural networks to dynamic, real-world scenarios where rules change with context, offering a solution for more adaptive AI systems.
The paper tackles the problem of deep neural networks being limited to fixed input-output mappings by developing a novel approach that enables continual learning of context-dependent rules with minimal samples per context. The result is a single network that can acquire numerous mapping rules online, requiring only about 10 samples to learn each.
Deep neural networks (DNNs) are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but changing according to different contexts. To lift such limits, we developed a novel approach involving a learning algorithm, called orthogonal weights modification (OWM), with the addition of a context-dependent processing (CDP) module. We demonstrated that with OWM to overcome the problem of catastrophic forgetting, and the CDP module to learn how to reuse a feature representation and a classifier for different contexts, a single network can acquire numerous context-dependent mapping rules in an online and continual manner, with as few as $\sim$10 samples to learn each. This should enable highly compact systems to gradually learn myriad regularities of the real world and eventually behave appropriately within it.