Generative AI models for businesses threaten to upend the world of content creation, with substantial impacts on marketing, software, design, entertainment, and interpersonal communications. These models are able to produce text and images: blog posts, program code, poetry, and artwork. The software uses complex machine learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images. Companies need to understand how these tools work, and how they can add value. Read More
Tag Archives: GANS
The Weird Science of Generative AI
… The current thing in tech no longer involves crypto. Suddenly, all the hype is filtering toward generative AI, broadly defined as artificial intelligence that doesn’t just process preexisting data sets, but creates wholly original text, images, audio, videos and code. In this week’s cover story, Margaux assessed the emerging leaders of the text- image-, video-, audio- and data-generating pack, some of which are already worth billions.
We’re living through the palace revolution of artificial intelligence. Creatives, once thought immune to the looming threat of AI, are suddenly competing with software like Open AI’s Dall-E and Jasper’s text-generation platform, which use neural networks to generate original images and text. Annie and Arielle identified 14 creators who are among the first to experiment with the new generative AI tools—often with dazzling results. Read More
Fighting AI with AI: The Battle against Deepfakes
NEARLY A DECADE AGO, Ian Goodfellow, then a PhD candidate at Université de Montréal, was drinking with friends at the 3 Brasseurs in Montreal’s downtown when he conceived an idea that would change machine learning—and the world of disinformation—forever.
“I don’t want to be someone who goes around promoting alcohol for the purposes of science, but in this case, I do actually think that drinking helped a little bit,” said Goodfellow in his appearance on the Lex Fridman Podcast. If the idea came to him at lunchtime rather than over a beer in the evening, he added, he might have been able to talk himself out of it. Instead, he went home and started working on the project.
Goodfellow suspected that pitting two computer systems against each other—called generative adversarial networks, or GANs—would yield more realistic outputs than the deep-learning machines that existed at the time, which would often generate blurry images of people, usually with missing facial features, did. His early model was able to create numbers that looked hand drawn, human-like faces, and photos of animals that resembled something out of a pixelated Monet painting, but as the technology evolved, it became possible to create strikingly realistic forgeries using a much less involved process. Read More
Impressive photo restoration by AI !
This new and completely free AI model can fix most of your old pictures in a split second!
Do you also have old pictures of yourself or close ones that didn’t age well or that you, or your parents, took before we could produce high-quality images? I do, and I felt like those memories were damaged forever. Boy, was I wrong!
This new and completely free AI model can fix most of your old pictures in a split second. It works well even with very low or high-quality inputs, which is typically quite the challenge.
This week’s paper called Towards Real-World Blind Face Restoration with Generative Facial Prior tackles the photo restoration task with outstanding results. What’s even cooler is that you can try it yourself and in your preferred way. They have open-sourced their code, created a demo and online applications for you to try right now. If the results you’ve seen above aren’t convincing enough, just watch the video and let me know what you think in the comments, I know it will blow your mind! Read More
Stitch it in Time GAN
Nvidia’s upgraded AI art tool turned my obscure squiggles into a masterpiece
It’s incredible, the things we can do with AI nowadays. For artists looking to integrate artificial intelligence into their workflow, there are ever more advanced tools popping up all over the net. One such tool is Nvidia Canvas, which has just been updated with the more powerful GauGAN2 AI, to replace the original GauGAN model, along with loads of new features.
The Nvidia Canvas software is available for free to anyone with an Nvidia RTX graphics card. This is because the software uses the tensor cores present in your GPU to let the AI do it’s job. Read More
‘Paint Me a Picture’: NVIDIA Research Shows GauGAN AI Art Demo Now Responds to Words
GauGAN2 uses a deep learning model that turns a simple written phrase, or sentence, into a photorealistic masterpiece.
A picture worth a thousand words now takes just three or four words to create, thanks to GauGAN2, the latest version of NVIDIA Research’s wildly popular AI painting demo.
The deep learning model behind GauGAN allows anyone to channel their imagination into photorealistic masterpieces — and it’s easier than ever. Simply type a phrase like “sunset at a beach” and AI generates the scene in real time. Add an additional adjective like “sunset at a rocky beach,” or swap “sunset” to “afternoon” or “rainy day” and the model, based on generative adversarial networks, instantly modifies the picture.
With the press of a button, users can generate a segmentation map, a high-level outline that shows the location of objects in the scene. From there, they can switch to drawing, tweaking the scene with rough sketches using labels like sky, tree, rock and river, allowing the smart paintbrush to incorporate these doodles into stunning images. Read More
This Person (Probably) Exists. IdentityMembership Attacks Against GAN GeneratedFaces.
Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers. Indeed, the popular tongue-in-cheek website http://thispersondoesnotexist.com, taunts users with GAN generated images that seem too real to believe. On the otherhand, GANs do leak information about their training data, as evidenced by membership attacks recently demonstrated in the literature. In this work, we challenge the assumption that GAN faces really are novel creations, by constructing a successful membership attack of a new kind. Unlike previous works, our attack can accurately discern samples sharing the same identity as training samples without being the same samples. We demonstrate the interest of our attack across several popular face datasets and GAN training procedures. Notably, we show that even in the presence of significant dataset diversity, an over represented person can pose a privacy concern. Read More
GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)
We show how to learn a map that takes a content code, derived from a face image, and a randomly chosen style code to an anime image. We derive an adversarial loss from our simple and effective definitions of style and content. This adversarial loss guarantees the map is diverse – a very wide range of anime can be produced from a single content code. Under plausible assumptions, the map is not just diverse, but also correctly represents the probability of an anime, conditioned on an input face. In contrast, current multimodal generation procedures cannot capture the complex styles that appear in anime. Extensive quantitative experiments support the idea the map is correct. Extensive qualitative results show that the method can generate a much more diverse range of styles than SOTA comparisons. Finally, we show that our formalization of content and style allows us to perform video to video translation without ever training on videos Read More
#gans, #image-recognitionA way to spot computer-generated faces
A small team of researchers from The State University of New York at Albany, the State University of New York at Buffalo and Keya Medical has found a common flaw in computer-generated faces by which they can be identified. The group has written a paper describing their findings and have uploaded them to the arXiv preprint server.
…The researchers note that in many cases, users can simply zoom in on the eyes of a person they suspect may not be real to spot the pupil irregularities. They also note that it would not be difficult to write software to spot such errors and for social media sites to use it to remove such content. Unfortunately, they also note that now that such irregularities have been identified, the people creating the fake pictures can simply add a feature to ensure the roundness of pupils. Read More
Rick's Cafe AI 