AutoML GAN AutoGAN! AI Can Now Design Better GAN Models Than Humans

Thanks to the creation of AutoML -- which is essentially automated neural architecture search (NAS) -- AI can now design better deep neural networks than human researchers for computer vision tasks such as image classification and object detection. AutoML's tremendous success has prompted AI researchers to explore its efficacy in additional areas, such as generative adversarial networks (GANs). Researchers from Texas A&M University and MIT-IBM Watson AI Lab recently presented a paper that applies NAS to GANs. Their "AutoGAN" is an architecture search scheme specifically tailored for GANs that outperforms current state-of-the-art hand-crafted GANs on the task of unconditional image generation. The associated paper has been accepted by ICCV 2019.