PathNet is a modular deep learning architecture that utilizes multiple paths through the network to allow different parts of the network to specialize in different tasks. It addresses the problem of catastrophic forgetting in continual learning by freezing the weights of successful paths for particular tasks and allowing new paths to be created for new tasks. This enables knowledge reuse and efficient learning across a sequence of tasks. It essentially involves dynamically routing inputs through a network of modules, where different paths can be selected based on the task being performed. However, information on real-world usage is relatively limited compared to other deep learning architectures.
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