4 citations · 14 across the 38 of their papers we have counts for
5 papers · 1 filter
StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation
Umut Kocasari, Alara Dirik, Mert Tiftikci +1
Discovering meaningful directions in the latent space of GANs to manipulate semantic attributes typically requires large amounts of labeled data. Recent work aims to overcome this…
Exploring Latent Dimensions of Crowd-sourced Creativity
Umut Kocasari, Alperen Bag, Efehan Atici +1
Recently, the discovery of interpretable directions in the latent spaces of pre-trained GANs has become a popular topic. While existing works mostly consider directions for semanti…
Controlled Cue Generation for Play Scripts
Alara Dirik, Hilal Donmez, Pinar Yanardag
In this paper, we use a large-scale play scripts dataset to propose the novel task of theatrical cue generation from dialogues. Using over one million lines of dialogue and cues, w…
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…
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…