  We propose a novel mixture-of-experts class to optimize computer vision
models in accordance with data transfer limitations at test time. Our approach
postulates that the minimum acceptable amount of data allowing for
highly-accurate results can vary for different input space partitions.
Therefore, we consider mixtures where experts require different amounts of
data, and train a sparse gating function to divide the input space for each
expert. By appropriate hyperparameter selection, our approach is able to bias
mixtures of experts towards selecting specific experts over others. In this
way, we show that the data transfer optimization between visual sensing and
processing can be solved as a convex optimization problem.To demonstrate the
relation between data availability and performance, we evaluate biased mixtures
on a range of mainstream computer vision problems, namely: (i) single shot
detection, (ii) image super resolution, and (iii) realtime video action
classification. For all cases, and when experts constitute modified baselines
to meet different limits on allowed data utility, biased mixtures significantly
outperform previous work optimized to meet the same constraints on available
data.
