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LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
A framework that learns meaningful directions in GANs' latent space using unsupervised contrastive learning. Instead of discovering fixed directions such as in previous work, this method can discover non-linear directions in pretrained StyleGAN2 and BigGAN models. The discovered directions may be used for image manipulation.
Authors use the differences caused by an edit operation on the feature activations to optimize the identifiability of each direction. The edit operations are modeled by several separate neural nets
📝 Paper
🛠 Code (next week)
#paper_tldr #cv #gan
A framework that learns meaningful directions in GANs' latent space using unsupervised contrastive learning. Instead of discovering fixed directions such as in previous work, this method can discover non-linear directions in pretrained StyleGAN2 and BigGAN models. The discovered directions may be used for image manipulation.
Authors use the differences caused by an edit operation on the feature activations to optimize the identifiability of each direction. The edit operations are modeled by several separate neural nets
∆_i(z) and learning. Given a latent code z and its generated image x = G(z), we seek to find edit operations ∆_i(z) such that the image x' = G(∆_i(z)) has semantically meaningful changes over x while still preserving the identity of x.📝 Paper
🛠 Code (next week)
#paper_tldr #cv #gan
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
A framework that learns meaningful directions in GANs' latent space using unsupervised contrastive learning. Instead of discovering fixed directions such as in previous work, this method can discover non-linear directions in pretrained StyleGAN2 and BigGAN models. The discovered directions may be used for image manipulation.
Authors use the differences caused by an edit operation on the feature activations to optimize the identifiability of each direction. The edit operations are modeled by several separate neural nets
📝 Paper
🛠 Code (next week)
#paper_tldr #cv #gan
A framework that learns meaningful directions in GANs' latent space using unsupervised contrastive learning. Instead of discovering fixed directions such as in previous work, this method can discover non-linear directions in pretrained StyleGAN2 and BigGAN models. The discovered directions may be used for image manipulation.
Authors use the differences caused by an edit operation on the feature activations to optimize the identifiability of each direction. The edit operations are modeled by several separate neural nets
∆_i(z) and learning. Given a latent code z and its generated image x = G(z), we seek to find edit operations ∆_i(z) such that the image x' = G(∆_i(z)) has semantically meaningful changes over x while still preserving the identity of x.📝 Paper
🛠 Code (next week)
#paper_tldr #cv #gan
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