GANSpace is a framework that enables the discovery of interpretable latent directions in Generative Adversarial Networks (GANs), facilitating intuitive control over image generation. By applying Principal Component Analysis (PCA) to the activation space of GANs, it identifies directions corresponding to semantic attributes like age, lighting, and viewpoint. This approach allows for layer-wise manipulation of GANs, providing users with fine-grained control over generated images.
Key Features
Identification of interpretable latent directions using PCA.
Layer-wise manipulation of GANs for fine-grained control.
Support for BigGAN and StyleGAN architectures.
Interactive exploration of latent spaces.
Visualization tools for understanding latent directions.
Compatibility with various GAN models trained on diverse datasets.
Android DevHub
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