  We propose a method for specializing deep detectors and trackers to
restricted settings. Our approach is designed with the following goals in mind:
(a) Improving accuracy in restricted domains; (b) preventing overfitting to new
domains and forgetting of generalized capabilities; (c) aggressive model
compression and acceleration. To this end, we propose a novel loss that
balances compression and acceleration of a deep learning model vs. loss of
generalization capabilities. We apply our method to the existing tracker and
detector models. We report detection results on the VIRAT and CAVIAR data sets.
These results show our method to offer unprecedented compression rates along
with improved detection. We apply our loss for tracker compression at test
time, as it processes each video. Our tests on the OTB2015 benchmark show that
applying compression during test time actually improves tracking performance.
