xrenaa/DisCo
[ICLR2022] Code for "Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View"
DisCo helps AI researchers and practitioners analyze the factors that make up generated images. Given a pre-trained generative model (like GANs, VAEs, or Flow models) and a collection of generated images, it can identify and extract distinct, understandable attributes, such as 'pose' or 'smile' in faces, or 'object color' in shapes. This allows researchers to understand how these models create images and to manipulate specific features.
136 stars. No commits in the last 6 months.
Use this if you need to understand the underlying, independent factors of variation within images generated by a deep learning model, without needing to manually label your data.
Not ideal if you are looking to generate new images from scratch or if you primarily work with real-world images that haven't been synthesized by a generative model.
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136
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Language
Python
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Last pushed
Mar 24, 2022
Commits (30d)
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