  The search for efficient neural network architectures has gained much focus
in recent years, where modern architectures focus not only on accuracy but also
on inference time and model size. Here, we present FUN, a family of novel
Frequency-domain Utilization Networks. These networks utilize the inherent
efficiency of the frequency-domain by working directly in that domain,
represented with the Discrete Cosine Transform. Using modern techniques and
building blocks such as compound-scaling and inverted-residual layers we
generate a set of such networks allowing one to balance between size, latency
and accuracy while outperforming competing RGB-based models. Extensive
evaluations verifies that our networks present strong alternatives to previous
approaches. Moreover, we show that working in frequency domain allows for
dynamic compression of the input at inference time without any explicit change
to the architecture.
