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Semi-Supervised StyleGAN for Disentanglement Learning

Weili Nie · Tero Karras · Animesh Garg · Shoubhik Debnath · Anjul Patney · Ankit Patel · Anima Anandkumar

Keywords: [ Deep Generative Models ] [ Generative Adversarial Networks ] [ Representation Learning ] [ Semi-supervised learning ] [ Deep Learning - Generative Models and Autoencoders ]


Disentanglement learning is crucial for obtaining disentangled representations and controllable generation. Current disentanglement methods face several inherent limitations: difficulty with high-resolution images, primarily focusing 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.

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