Microsoft Research Unveils Three Efforts to Advance Deep Generative Models

Optimus, FQ-GAN and Prevalent bring new ideas to apply generative models at large scale.

Generative models have been an important component of machine learning for the last few decades. With the emergence of the deep learning, generative models started being combined with deep neural networks creating the field of deep generative models(DGMs). DGMs hold a lot of promise for the deep learning field as they have the ability of synthesizing data from observation. This feature can result key to improve the training of large scale models without requiring large amounts of data. Recently, Microsoft Research unveiled three new projects looking to advance research in DGMs.

One of the biggest questions surrounding DGMs is whether they can be applied with large-scale datasets. In recent years, we have seen plenty of examples of DGMs applied in a relatively small scale. However, the deep learning field is gravitating towards a “bigger is better” philosophy when comes to data and we are regularly seeing new models being trained in unfathomably big datasets. The idea of DGMs that can operate at that scale is one of the most active areas of research in the space and the focus of the Microsoft Research projects.