AI Podcast: The 411 on NVIDIA Research

by Allison Toh

De-noising, semantic manipulation and unsupervised text modeling. These are only some of the projects that our NVIDIA Research team has been tackling for the past several months.

In the latest AI Podcast episode, Bryan Catanzaro, vice president of applied deep learning research at NVIDIA, gives a full rundown of the group’s recent discoveries and shares what else is in store for NVIDIA Research.

“The goal of NVIDIA research is to figure out what things are going to change the future of the company, and then build prototypes that show the company how to do that,” Catanzaro said in a conversation with AI Podcast host Noah Kravitz. “And AI is a good example of that.”

Noise! Noise! Noise!

Developed by NVIDIA’s research team in Sweden and Finland, the Noise2Noise project discovered that a matching set of images is not necessary to solve de-noising problems.

“So people have been working on de-noising for a while,” Catanzaro said. “And the insight that led to this Noise2Noise de-noiser is that you don’t actually need the clean image in order to do this.”

The standard AI de-noising method requires paired sets of identical images — half the batch is clean, the other half is noisy duplicates.

In many cases, clean images are inaccessible. So ultimately, you are trying to train the neural network to reproduce perfect images, even though it was only given noisy ones.

“As long as you have multiple copies of the same image, or a very similar image, where the noise is different, then you can train the model on all those noisy images and it will learn to remove the noise nonetheless,” Catanzaro said.

That’s Just Semantics

Building whole new virtual worlds just got a lot easier with semantic manipulation.

According to Catanzaro, the technique relies on a trained generative model to create photorealistic outputs with only high-level semantic descriptions of a scene.

And the possibilities with this are endless.

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“It opens the door to a lot of new techniques for rendering graphics, as well as manipulating images,” Catanzaro explained. “For example, in image editing, if I was able to change the type of object in an image, I could do that at a very broad scale. I could have a huge, big paintbrush and just sort of paint trees onto an image. And where I painted the trees it knows how to draw trees that fit there.”

Semantic manipulation is still in the research prototype stage, but the NVIDIA Research team open-sourced the project and discovered people were using the tool to create artificial satellite imagery.

AI and Beyond

Other projects NVIDIA Research has in store include unsupervised text modeling, in which the team is trying to develop a model that can indicate whether a section of text contains either positive or negative sentiment.

By training the model on an NVIDIA DGX system, “we can do things that, in the original paper that got published a few months ago took a month to do, we can do that in less than a day,” said Catanzaro.

Looking back, Catanzaro was surprised at how quickly AI has grown.

“Nowadays I think we’re just at the beginning,” said Catanzaro. “I see so many amazing opportunities just waiting for people to pick up, that I think we’re going to be finding really interesting, really valuable things to do with AI for quite some time to come.”

And when asked how NVIDIA managed to successfully adopt AI at the right time, he remarks:

“I think the part of the story that people sometimes miss out on is that NVIDIA prepared itself for this change by doing the research.”

For more information about how our researchers are revolutionizing graphics, see the papers (listed below) or read our related articles, “NVIDIA Research Brings AI to Graphics” and “NVIDIA Researchers Showcase Major Advances in Deep Learning at NIPS.”