Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors
Probabilistic generative models provide a flexible and systematic framework for learning the
underlying geometry of data. However, model selection in this setting is challenging …
underlying geometry of data. However, model selection in this setting is challenging …
Tc-vae: Uncovering out-of-distribution data generative factors
Uncovering data generative factors is the ultimate goal of disentanglement learning.
Although many works proposed disentangling generative models able to uncover the …
Although many works proposed disentangling generative models able to uncover the …
Measuring hallucination in disentangled representations
Disentanglement is a key challenge in representation learning as it may enable several
downstream tasks including edition operation at a high semantic level or privacy-preserving …
downstream tasks including edition operation at a high semantic level or privacy-preserving …
Gradient Adjusting Networks for Domain Inversion
StyleGAN2 was demonstrated to be a powerful image generation engine that supports
semantic editing. However, in order to manipulate a real-world image, one first needs to be …
semantic editing. However, in order to manipulate a real-world image, one first needs to be …