  Feature compression is a promising direction for coding for machines.
Existing methods have made substantial progress, but they require designing and
training separate neural network models to meet different specifications of
compression rate, performance accuracy and computational complexity. In this
paper, a flexible variable-rate feature compression method is presented that
can operate on a range of rates by introducing a rate control parameter as an
input to the neural network model. By compressing different intermediate
features of a pre-trained vision task model, the proposed method can scale the
encoding complexity without changing the overall size of the model. The
proposed method is more flexible than existing baselines, at the same time
outperforming them in terms of the three-way trade-off between feature
compression rate, vision task accuracy, and encoding complexity. We have made
the source code available at
https://github.com/adnan-hossain/var_feat_comp.git.
