  Pre-trained convolutional neural networks (CNNs) are powerful off-the-shelf
feature generators and have been shown to perform very well on a variety of
tasks. Unfortunately, the generated features are high dimensional and expensive
to store: potentially hundreds of thousands of floats per example when
processing videos. Traditional entropy based lossless compression methods are
of little help as they do not yield desired level of compression, while general
purpose lossy compression methods based on energy compaction (e.g. PCA followed
by quantization and entropy coding) are sub-optimal, as they are not tuned to
task specific objective. We propose a learned method that jointly optimizes for
compressibility along with the task objective for learning the features. The
plug-in nature of our method makes it straight-forward to integrate with any
target objective and trade-off against compressibility. We present results on
multiple benchmarks and demonstrate that our method produces features that are
an order of magnitude more compressible, while having a regularization effect
that leads to a consistent improvement in accuracy.
