Posted on October 10, 2019 | Back to Showreel

Improving sample diversity of a pre-trained, class-conditional GAN by changing its class embeddings

Tags: computer-vision, generative, technical, art | Paper

This is mostly a technical result, but it’s really neat for the art applications. One of the problems with a lot of generative networks is that they lack “diversity”. This means that most of the time, the kinds of pictures they generate are mostly the same. This work address this problem, and even more impressively, it addresses it without the need to train the model again!

Very cool.