Search Results for author:
Found 2 papers, 1 papers with code
Fantastic Style Channels and Where to Find Them: A Submodular Framework for Discovering Diverse Directions in GANs
We evaluate our framework with qualitative and quantitative experiments and show that our method finds more diverse and disentangled directions.
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
