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Found 4 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.
Discovering Multiple and Diverse Directions for Cognitive Image Properties
While previous work has focused on discovering a single direction that performs a desired editing operation such as zoom-in, limited work has been done on the discovery of multiple and diverse directions that can achieve the desired edit.
StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation
Discovering meaningful directions in the latent space of GANs to manipulate semantic attributes typically requires large amounts of labeled data.
Exploring Latent Dimensions of Crowd-sourced Creativity
We explore the latent dimensions of images generated on this platform and present a novel framework for manipulating images to make them more creative.
