https://github.com/google/explaining-in-style

https://github.com/google/explaining-in-style

Last synced: 9 days ago
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• Host: GitHub
• URL: https://github.com/google/explaining-in-style
• Owner: google
• License: apache-2.0
• Created: 2021-08-25T16:17:00.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-08-25T18:00:58.000Z (about 3 years ago)
• Last Synced: 2024-08-02T15:19:39.963Z (3 months ago)
• Language: Jupyter Notebook
• Size: 14.6 KB
• Stars: 219
• Watchers: 8
• Forks: 27
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
## Explaining in Style: Official TensorFlow Colab

**Explaining in Style: Training a GAN to explain a classifier in

StyleSpace**


Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan

Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani and

Inbar Mosseri.


Paper: https://arxiv.org/abs/2104.13369

Abstract:

*Image classification models can depend on multiple different semantic attributes

of the image. An explanation of the decision of the classifier needs to both

discover and visualize these properties. Here we present StylEx, a method for

doing this, by training a generative model to specifically explain multiple

attributes that underlie classifier decisions. A natural source for such

attributes is the StyleSpace of StyleGAN, which is known to generate

semantically meaningful dimensions in the image. However, because standard GAN

training is not dependent on the classifier, it may not represent these

attributes which are important for the classifier decision, and many dimensions

of StyleSpace may represent irrelevant attributes. To overcome this, we propose

a training procedure for a StyleGAN, which incorporates the classifier model, in

order to learn a classifier-specific StyleSpace. Explanatory attributes are then

selected from this space. These can be used to visualize the effect of changing

multiple attributes per image, thus providing image-specific explanations. We

apply StylEx to multiple domains, including animals, leaves, faces and retinal

images. For these, we show how an image can be modified in different ways to

change its classifier output. Our results show that the method finds attributes

that align well with semantic ones, generate meaningful image-specific

explanations, and are human-interpretable as measured in user-studies. *

## About this colab

Use this colab to load the weights of a pre-trained StyleGAN2 model trained on

age classifier, and to find and manipulate the Style indices which correspond to

the most important attributes for this classifier. The colab has an

implementation of the AttFind algorithm from the paper, and has utilities to

visualize these attributes.

## License

This colab is licensed under the terms of the Apache license. See [LICENSE](LICENSE)

for more information.

## Mandatory Disclaimer

This is not an officially supported Google product.