Unsupervised Approach for GAN Interpretability Through Semantic Direction Discovery | Synced
Researchers have introduced the first unsupervised learning approach for identifying interpretable semantic directions in the latent space of generative adversarial network (GAN) models.
Source: Synced | AI Technology & Industry Review
Researchers have introduced the first unsupervised learning approach for identifying interpretable semantic directions in the latent space of generative adversarial network (GAN) models.