identity loss

Identity loss is a concept used in machine learning, especially in models that generate or transform images. It ensures that when an image is modified—like changing the style or adding effects—the core identity or essence of the original object remains unchanged. For example, if you apply a style transfer to a person's face, the person's unique features should stay recognizable. The identity loss measures how much the altered image deviates from the original in identifying features, guiding the model to produce changes that preserve the fundamental identity while allowing creative transformations.