3 papers
cs.CV2022
Fantastic Style Channels and Where to Find Them: A Submodular Framework for Discovering Diverse Directions in GANs
Enis Simsar, Umut Kocasari, Ezgi Gülperi Er +1
The discovery of interpretable directions in the latent spaces of pre-trained GAN models has recently become a popular topic. In particular, StyleGAN2 has enabled various image gen…
cs.CV2021
Graph2Pix: A Graph-Based Image to Image Translation Framework
Dilara Gokay, Enis Simsar, Efehan Atici +3
In this paper, we propose a graph-based image-to-image translation framework for generating images. We use rich data collected from the popular creativity platform Artbreeder (http…
cs.LG2021
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
Oğuz Kaan Yüksel, Enis Simsar, Ezgi Gülperi Er +1
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs). These directions enable c…