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Neural networks have seen an explosion of usage and research in the pastdecade, particularly within the domains of computer vision and natural languageprocessing. However, only recently have advancements in neural networks yieldedperformance improvements beyond narrow applications and translated to expandedmultitask models capable of generalizing across multiple data types andmodalities. Simultaneously, it has been shown that neural networks areoverparameterized to a high degree, and pruning techniques have proved capableof significantly reducing the number of active weights within the network whilelargely preserving performance. In this work, we identify a methodology andnetwork representational structure which allows a pruned network to employpreviously unused weights to learn subsequent tasks. We employ thesemethodologies on well-known benchmarking datasets for testing purposes and showthat networks trained using our approaches are able to learn multiple tasks,which may be related or unrelated, in parallel or in sequence withoutsacrificing performance on any task or exhibiting catastrophic forgetting.

The Multiple Subnetwork Hypothesis: Enabling Multidomain Learning by Isolating Task-Specific Subnetworks in Feedforward Neural Networks