The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang12
Phillip Isola13
Alexei A. Efros1
Eli Shechtman2
Oliver Wang2
1UC Berkeley
2Adobe Research
Code [GitHub]
CVPR 2018 [Paper]


While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.

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R. Zhang, P. Isola, A. A. Efros,
E. Shechtman, O. Wang.

The Unreasonable Effectiveness of
Deep Features as a Perceptual Metric.

In CVPR, 2018 [ArXiv (preferred)] [CVF].




This research was supported, in part, by grants from Berkeley Deep Drive, NSF IIS-1633310, and hardware donations by NVIDIA. We thank members of the Berkeley AI Research Lab and Adobe Research for helpful discussions. We thank Alan Bovik for his insightful comments. We also thank Radu Timofte, Zhaowen Wang, Michael Waechter, Simon Niklaus, and Sergio Guadarrama for help preparing data. RZ is partially supported by an Adobe Research Fellowship and much of this work was done while RZ was an intern at Adobe Research.