A Style-Based Generator Architecture for Generative Adversarial Networks
- Category: Article
- Created: February 17, 2022 3:46 PM
- Status: Open
- URL: https://arxiv.org/pdf/1812.04948.pdf
- Updated: February 17, 2022 6:07 PM
Reference: https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans-generating-and-tuning-realistic-6cb2be0f431
Highlights
- The new generator architecture leads to an automatically learned, unsupervised separation of high-level attributes (eg. pose and identity when trained on human faces) and stochastic variation in the generated images (freckles, hair). and it enables intuitive, scale-specific control of the synthesis.
- The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation.
Methods
Mapping Network
Given a latent code z in the input latent space \(z\), a non-linear mapping network \(f: z \rightarrow w\) first produces \(\mathbf{w} \in \mathcal{W}\). For simplicity, we set the dimensionality of both spaces to 512, and the mapping f is implemented using an 8-layer MLP.