3/15/2024 0 Comments Photosketcher review![]() ![]() ![]() Experiments have shown that our method can mold GANs to match shapes and poses specified by sketches while maintaining realism and diversity. Furthermore, we explore different regularization methods to preserve the original model’s diversity and image quality. We encourage the model’s output to match the user sketches through a cross-domain adversarial loss. In particular, we change the weights of an original GAN model according to user sketches. In this work, we present a method, GAN Sketching, for rewriting GANs with one or more sketches, to make GANs training easier for novice users. In contrast, sketching is possibly the most universally accessible way to convey a visual concept. Can a user create a deep generative model by sketching a single example? Traditionally, creating a GAN model has required the collection of a large-scale dataset of exemplars and specialized knowledge in deep learning. ![]()
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