  We present a method for projecting an input image into the space of a
class-conditional generative neural network. We propose a method that optimizes
for transformation to counteract the model biases in generative neural
networks. Specifically, we demonstrate that one can solve for image
translation, scale, and global color transformation, during the projection
optimization to address the object-center bias and color bias of a Generative
Adversarial Network. This projection process poses a difficult optimization
problem, and purely gradient-based optimizations fail to find good solutions.
We describe a hybrid optimization strategy that finds good projections by
estimating transformations and class parameters. We show the effectiveness of
our method on real images and further demonstrate how the corresponding
projections lead to better editability of these images.
