: Learned latent space trajectories in generative adversarial networks correspond to visual transformations like camera shift and zoom. These transformations can change the distribution of generated data, but only so much -biases in the data, like centered objects, reveal themselves as objects get "stuck" at the image borders when we try to shift them out of frame. Take the "steering wheel", drive in the latent space, and explore the natural image manifold via generative transformations! An open secret in contemporary machine learning is that many models work beautifully on standard benchmarks but fail to generalize outside the lab. This has been attributed to training on biased data, which provide poor coverage over real world events. Generative models are no exception, but recent advances in generative adversarial networks (GANs) suggest otherwise -these models can now synthesize strikingly realistic and diverse images. Is generative modeling of photos a solved problem? We show that although current GANs can fit standard datasets very well, they still fall short of being comprehensive models of the visual manifold. In particular, we study their ability to fit simple transformations such as camera movements and color changes. We find that the models reflect the biases of the datasets on which they are trained (e.g., centered objects), but that they also exhibit some capacity for generalization: by "steering" in latent space, we can shift the distribution while still creating realistic images. We hypothesize that the degree of distributional shift is related to the breadth of the training data distribution, and conduct experiments that demonstrate this. Code is released on our project page: https://ali-design.github.io/ gan_steerability/
Exploring the latent space of GANs for image editing has been recently explored in Jahanian et al. REF .
On the"steerability"of generative adversarial networks
ArXiv
ArXiv
Computer Science