  Convolutional neural networks (CNN) are increasingly used in many areas of
computer vision. They are particularly attractive because of their ability to
"absorb" great quantities of labeled data through millions of parameters.
However, as model sizes increase, so do the storage and memory requirements of
the classifiers. We present a novel network architecture, Frequency-Sensitive
Hashed Nets (FreshNets), which exploits inherent redundancy in both
convolutional layers and fully-connected layers of a deep learning model,
leading to dramatic savings in memory and storage consumption. Based on the key
observation that the weights of learned convolutional filters are typically
smooth and low-frequency, we first convert filter weights to the frequency
domain with a discrete cosine transform (DCT) and use a low-cost hash function
to randomly group frequency parameters into hash buckets. All parameters
assigned the same hash bucket share a single value learned with standard
back-propagation. To further reduce model size we allocate fewer hash buckets
to high-frequency components, which are generally less important. We evaluate
FreshNets on eight data sets, and show that it leads to drastically better
compressed performance than several relevant baselines.
