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richzhang authored Sep 29, 2019
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Expand Up @@ -78,7 +78,7 @@ File [`perceptual_loss.py`](perceptual_loss.py) shows how to iteratively optimiz

We found that deep network activations work surprisingly well as a perceptual similarity metric. This was true across network architectures (SqueezeNet [2.8 MB], AlexNet [9.1 MB], and VGG [58.9 MB] provided similar scores) and supervisory signals (unsupervised, self-supervised, and supervised all perform strongly). We slightly improved scores by linearly "calibrating" networks - adding a linear layer on top of off-the-shelf classification networks. We provide 3 variants, using linear layers on top of the SqueezeNet, AlexNet (default), and VGG networks.

**If you use LPIPS in your publication, please specify which version you are using.** The current version is 0.1. You can set `version='0.0'` for the initial release.
If you use LPIPS in your publication, please specify which version you are using. The current version is 0.1. You can set `version='0.0'` for the initial release.

## (2) Berkeley Adobe Perceptual Patch Similarity (BAPPS) dataset

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