Iterative Latent Code Optimization With Convergence Monitoring And Early Stopping

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold.

Unique: Implements adaptive learning rate scheduling based on loss plateau detection, automatically reducing learning rate when progress stalls. Combines motion supervision loss with a photorealism regularization term that penalizes large deviations from the original latent code, balancing edit magnitude with image quality.

vs others: More efficient than fixed-iteration optimization because early stopping prevents wasted computation, and more interpretable than black-box optimization because loss curves provide diagnostic information.