  Deep learning has achieved state-of-the-art performance on several computer
vision tasks and domains. Nevertheless, it still has a high computational cost
and demands a significant amount of parameters. Such requirements hinder the
use in resource-limited environments and demand both software and hardware
optimization. Another limitation is that deep models are usually specialized
into a single domain or task, requiring them to learn and store new parameters
for each new one. Multi-Domain Learning (MDL) attempts to solve this problem by
learning a single model that is capable of performing well in multiple domains.
Nevertheless, the models are usually larger than the baseline for a single
domain. This work tackles both of these problems: our objective is to prune
models capable of handling multiple domains according to a user defined budget,
making them more computationally affordable while keeping a similar
classification performance. We achieve this by encouraging all domains to use a
similar subset of filters from the baseline model, up to the amount defined by
the user's budget. Then, filters that are not used by any domain are pruned
from the network. The proposed approach innovates by better adapting to
resource-limited devices while, to our knowledge, being the only work that is
capable of handling multiple domains at test time with fewer parameters and
lower computational complexity than the baseline model for a single domain.

Computer Vision and Pattern Recognition