Blog

The latest from Google Research

TFGAN: A Lightweight Library for Generative Adversarial Networks

Tuesday, December 12, 2017

Posted by Joel Shor, Senior Software Engineer, Machine Perception(Crossposted on the Google Open Source Blog)image compressiontext-to-speech systemsGenerative Adversarial Networksgenerating images from textsuperresolutionhelping robots learn to grasp

A video of a generator improving over time. It begins by producing random noise, and eventually learns to generate MNIST digits.

TFGANexamplestutorial

This demonstrates the effect of an adversarial loss on image compression. The top row shows image patches from the ImageNet dataset. The middle row shows the results of compressing and uncompressing an image through an image compression neural network trained on a traditional loss. The bottom row shows the results from a network trained with a traditional loss and an adversarial loss. The GAN-loss images are sharper and more detailed, even if they are less like the original.

Most neural text-to-speech (TTS) systems produce over-smoothed spectrograms. When applied to the Tacotron TTS system, a GAN can recreate some of the realistic-texture, which reduces artifacts in the resulting audio.