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