LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
Abstract
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 controllable image generation and support a wide range of semantic editing operations, such as zoom or rotation. The discovery of such directions is often done in a supervised or semi-supervised manner and requires manual annotations which limits their use in practice. In comparison, unsupervised discovery allows finding subtle directions that are difficult to detect a priori. In this work, we propose a contrastive learning-based approach to discover semantic directions in the latent space of pre-trained GANs in a self-supervised manner. Our approach finds semantically meaningful dimensions compatible with state-of-the-art methods.
Cite
Text
Yüksel et al. "LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions." International Conference on Computer Vision, 2021. doi:10.1109/ICCV48922.2021.01400Markdown
[Yüksel et al. "LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions." International Conference on Computer Vision, 2021.](https://mlanthology.org/iccv/2021/yuksel2021iccv-latentclr/) doi:10.1109/ICCV48922.2021.01400BibTeX
@inproceedings{yuksel2021iccv-latentclr,
title = {{LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions}},
author = {Yüksel, Oğuz Kaan and Simsar, Enis and Er, Ezgi Gülperi and Yanardag, Pinar},
booktitle = {International Conference on Computer Vision},
year = {2021},
pages = {14263-14272},
doi = {10.1109/ICCV48922.2021.01400},
url = {https://mlanthology.org/iccv/2021/yuksel2021iccv-latentclr/}
}