AI-Generated Images
To date, much of deep learning has used supervised learning to provide machines a human-like object recognition capability. For example, supervised learning can do a good job telling the difference between a Corgi and a German Shepherd, and labeled images of both breeds are readily available for training.
To give machines a more “imaginative” capability, such as imagining how a wintery scene would look like in the summer, Liu and team used unsupervised learning and generative modeling. An example of their work is shown below, where the winter and sunny scenes on the left are the inputs and the imagined corresponding summer and rainy scenes are on the right.
The NVIDIA Research team’s work uses a pair of generative adversarial networks (GANs) with a shared latent space assumption to obtain these stunning results. Considering the top two images above, the first GAN is trained on the winter scene — overcast skies, bare trees, snow covering just about everything but the cars sailing down the frozen road. The second GAN is trained to understand generally what summer looks like, but hasn’t been trained on the same specific scene as its counterpart.
Via René Schulte:
In the near future we can’t trust any photos we see and you won’t need humans with superhuman Photoshop skills for that.
