  In real world scenarios, model accuracy is hardly the only factor to
consider. Large models consume more memory and are computationally more
intensive, which makes them difficult to train and to deploy, especially on
mobile devices. In this paper, we build on recent results at the crossroads of
Linear Algebra and Deep Learning which demonstrate how imposing a structure on
large weight matrices can be used to reduce the size of the model. We propose
very compact models for video classification based on state-of-the-art network
architectures such as Deep Bag-of-Frames, NetVLAD and NetFisherVectors. We then
conduct thorough experiments using the large YouTube-8M video classification
dataset. As we will show, the circulant DBoF embedding achieves an excellent
trade-off between size and accuracy.
