Semantic image editing allows users to selectively change entire image attributes in a controlled manner with just a few clicks. Most approaches use a generative adversarial network (GAN) for this task to learn an appropriate latent space representation and attribute-specific transformations. While earlier approaches often suffer from entangled attribute manipulations, newer ones improve on this aspect by using separate specialized networks for attribute extraction. Iterative optimization algorithms based on backpropagation constitute a possible approach to find attribute vectors with little entanglement. However, this requires a large amount of GPU memory, training instabilities can occur, and the used models have to be differentiable. To address these issues, we propose a local search-based approach for latent space editing. We show that it performs at the same level as previous algorithms and avoids these drawbacks.     «

Semantic image editing allows users to selectively change entire image attributes in a controlled manner with just a few clicks. Most approaches use a generative adversarial network (GAN) for this task to learn an appropriate latent space representation and attribute-specific transformations. While earlier approaches often suffer from entangled attribute manipulations, newer ones improve on this aspect by using separate specialized networks for attribute extraction. Iterative optimization algori...     »