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This work presents a method for adapting a single, fixed deep neural networkto multiple tasks without affecting performance on already learned tasks. Bybuilding upon ideas from network quantization and pruning, we learn binarymasks that piggyback on an existing network, or are applied to unmodifiedweights of that network to provide good performance on a new task. These masksare learned in an end-to-end differentiable fashion, and incur a low overheadof 1 bit per network parameter, per task. Even though the underlying network isfixed, the ability to mask individual weights allows for the learning of alarge number of filters. We show performance comparable to dedicated fine-tunednetworks for a variety of classification tasks, including those with largedomain shifts from the initial task (ImageNet), and a variety of networkarchitectures. Unlike prior work, we do not suffer from catastrophic forgettingor competition between tasks, and our performance is agnostic to task ordering.Code available at https://github.com/arunmallya/piggyback.

Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights