The perceptual loss has been widely used as an effective loss term in image
synthesis tasks including image super-resolution, and style transfer. It was
believed that the success lies in the high-level perceptual feature
representations extracted from CNNs pretrained with a large set of images. Here
we reveal that, what matters is the network structure instead of the trained
weights. Without any learning, the structure of a deep network is sufficient to
capture the dependencies between multiple levels of variable statistics using
multiple layers of CNNs. This insight removes the requirements of pre-training
and a particular network structure (commonly, VGG) that are previously assumed
for the perceptual loss, thus enabling a significantly wider range of
applications. To this end, we demonstrate that a randomly-weighted deep CNN can
be used to model the structured dependencies of outputs. On a few dense
per-pixel prediction tasks such as semantic segmentation, depth estimation and
instance segmentation, we show improved results of using the extended
randomized perceptual loss, compared to the baselines using pixel-wise loss
alone. We hope that this simple, extended perceptual loss may serve as a
generic structured-output loss that is applicable to most structured output
learning tasks.

研究结果表明，深度卷积神经网络 (CNNs) 的结构能够捕捉到多层次变量统计之间的依赖关系，从而消除了以往对于卷积神经网络预训练权重和特定网络结构（通常为 VGG) 的假设，提供了更广泛的应用空间。在实验中，使用扩展的随机感知损失可以更好地完成像素级预测任务，如语义分割，深度估计和实例分割。我们希望，这种简单的、扩展的感知损失可以成为适用于大多数结构化输出学习任务的通用结构化输出损失。