Latest Model That Might Replace GANs To Create Deepfakes

Recently, a team of researchers from UC Berkeley and Adobe Research proposed a new machine learning model known as Swapping Autoencoder, which has the capability to perform image manipulation. The key idea of this research is to encode a picture into 2 independent components and then enforce that any swapped combination maps to a realistic image. Deep generative models such as GANs or Generative Adversarial Networks and Variational Autoencoders (VAEs) have gained much traction by the researchers over the years. According to the researchers, deep generative models have become a popular technique when it comes to producing realistic images from randomly sampled data. However, such deep generative models face various challenges when used for a controllable manipulation of existing images.