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Semi-Supervised StyleGAN for Disentanglement Learning

Nie, Weili and Karras, Tero and Garg, Animesh and Debhath, Shoubhik and Patney, Anjul and Patel, Ankit B. and Anandkumar, Anima (2020) Semi-Supervised StyleGAN for Disentanglement Learning. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200402-135651804

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Abstract

Disentanglement learning is crucial for obtaining disentangled representations and controllable generation. Current disentanglement methods face several inherent limitations: difficulty with high-resolution images, primarily on learning disentangled representations, and non-identifiability due to the unsupervised setting. To alleviate these limitations, we design new architectures and loss functions based on StyleGAN (Karras et al., 2019), for semi-supervised high-resolution disentanglement learning. We create two complex high-resolution synthetic datasets for systematic testing. We investigate the impact of limited supervision and find that using only 0.25%~2.5% of labeled data is sufficient for good disentanglement on both synthetic and real datasets. We propose new metrics to quantify generator controllability, and observe there may exist a crucial trade-off between disentangled representation learning and controllable generation. We also consider semantic fine-grained image editing to achieve better generalization to unseen images.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2003.03461arXivDiscussion Paper
Record Number:CaltechAUTHORS:20200402-135651804
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200402-135651804
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102273
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:02 Apr 2020 21:02
Last Modified:02 Apr 2020 21:02

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