Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence
  of Training Data
Tashi Namgyal Alexander Hepburn Raul Santos-Rodriguez Valero Laparra Jesus Malo
Perceptual metrics are traditionally used to evaluate the quality of natural
signals, such as images and audio. They are designed to mimic the perceptual
behaviour of human observers and usually reflect structures found in natural
signals. This motivates their use as loss functions for training generative
models such that models will learn to capture the structure held in the metric.
We take this idea to the extreme in the audio domain by training a compressive
autoencoder to reconstruct uniform noise, in lieu of natural data. We show that
training with perceptual losses improves the reconstruction of spectrograms and
re-synthesized audio at test time over models trained with a standard Euclidean
loss. This demonstrates better generalisation to unseen natural signals when
using perceptual metrics.