Pinar Yanardag

Featuring a dress and a necklace generated by GANs -- made by human designers at Conference of the Future'19, Chile.

I am a tenure-track assistant professor at Virginia Tech, Department of Computer Science where I lead 💎 GEMLAB. I am also a member of Sanghani Center for AI and Discovery Analytics.

Prior to Virginia Tech, I was a postdoc at MIT. I received my Ph.D. in Computer Science at Purdue. During my graduate studies, I also worked at Amazon (P13N team) and VMware. I am a Fulbright PhD Fellow and Google Anita Borg Memorial Scholar.

My research is published at top computer science conferences such as NeurIPS, ICCV and KDD, and featured in mainstream media (e.g., The Washington Post, BBC, CNN) and magazines (e.g., Motherboard, Rolling Stone). For more information, see the Publications page.

My main research area is centered on the development of generative AI methods, targeting three key aspects:

Interpretability and Explainability; demystifying the complex nature of generative models, often seen as ‘black boxes’, and making them more transparent and understandable.
Creativity and Human-AI Collaboration; designing new paradigms of interaction to fully harness the potential of generative AI and enhance the creative process.
Fairness & Bias; Identifying and mitigating biases in generative models to move towards a fairer and more ethical AI.

Prior to joining Virginia Tech, I was CEO of AI Fiction, a creative design studio specializing in artificial intelligence. Some of our work includes generative AI for HBO’s Westworld, for which I was a Creative Director nominee at 72nd Primetime Emmy Awards. I also co-founded GLITCH–the world’s first generative AI clothing line.

I am deeply passionate about promoting the understanding and appreciation of generative models among the general public. At MIT, I started How to Generate (Almost) Anything project, which demystify generative AI through collaboration with artists and artisans, thereby encouraging a broader dialogue about the future of generative AI and its everyday implications. I also taught “AI & Fashion” at London College of Fashion, further contributing to this educational pursuit. See Courses section for more information.

Please contact me via pinary at vt.edu.

news

Jan 22, 2023	Deep Empathy is featured on New York Times!
Aug 15, 2022	One paper is accepted to ECCV’22, two papers are accepted to WACV’23!
Apr 1, 2022	Invited talk at CMU Robotics Institute, watch live or visit on campus!
Mar 15, 2022	Invited talk at MIT EECS Vision & Graphics Seminar!
Feb 1, 2022	CATLAB undergraduate researchers Cemre and Eylul won the best senior thesis award!

selected publications

ECCV

FairStyle: Debiasing StyleGAN2 with Style Channel Manipulations

Cemre Efe Karakas*, Alara Dirik*, Eylul Yalcinkaya, and Pinar Yanardag

European Conference on Computer Vision 2022

Abstract PDF Website

Recent advances in generative adversarial networks have shown that it is possible to generate high-resolution and hyperrealistic images. However, the images produced by GANs are only as fair and representative as the datasets on which they are trained. In this paper, we propose a method for directly modifying a pre-trained StyleGAN2 model that can be used to generate a balanced set of images with respect to one (e.g., eyeglasses) or more attributes (e.g., gender and eyeglasses). Our method takes advantage of the style space of the StyleGAN2 model to perform disentangled control of the target attributes to be debiased. Our method does not require training additional models and directly debiases the GAN model, paving the way for its use in various downstream applications. Our experiments show that our method successfully debiases the GAN model within a few minutes without compromising the quality of the generated images.
ICCV

LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions

Oguz Kaan Yuksel, Enis Simsar, Ezgi Gulperi Er, and Pinar Yanardag

International Conference on Computer Vision (ICCV) 2021

Abstract PDF Code

Recent research has shown great potential for finding interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs). These directions provide controllable generation and support a wide range of semantic editing operations such as zoom or rotation. The discovery of such directions is often performed in a supervised or semi-supervised fashion and requires manual annotations, limiting their applications in practice. In comparison, unsupervised discovery enables finding subtle directions a priori hard to recognize. In this work, we propose a contrastive-learning-based approach for discovering semantic directions in the latent space of pretrained GANs in a self-supervised manner. Our approach finds semantically meaningful dimensions compatible with state-of-the-art methods.