Generative Adversarial Network (GAN)

In spoken conversation, people naturally take turns amid interjections and other patterns that aren’t strictly verbal. A new approach generated natural-sounding audio dialogs without training on text transcriptions that mark when one party should stop speaking and the other should chime in.

A homebrew re-creation of OpenAI’s DALL·E model is the latest internet sensation. Craiyon has been generating around 50,000 user-prompted images daily, thanks to its ability to produce visual mashups like Darth Vader ice fishing and photorealistic Pokemon characters.

If you want to both synthesize data and find the probability of any given example — say, generate images of manufacturing defects to train a defect detector and identify the highest-probability defects — you may use the architecture known as a normalizing flow.

Marketers are using fake social media personas — enhanced by AI-generated portraits — to expand their reach without busting their budgets.

A new deep learning technique increased the precision of short-term rainfall forecasts. Researchers developed the Deep Generative Model of Radar (DGMR) to predict amounts of precipitation up to two hours in advance.

Amazon’s first brick-and-mortar clothing store is getting ready to deliver automated outfit recommendations. The ecommerce giant announced plans to open a flagship Amazon Style location at a Los Angeles-area mall this year.

Things are really starting to get going in the field of AI. After many years (decades?!) of focusing on algorithms, the AI community is finally ready to accept the central role of data and the high-capacity models that are capable of taking advantage of this data.

Is human creativity being replaced by the synthetic equivalent? The fear: AI is cranking out increasingly sophisticated visual, musical, and literary works. AI-generated media will flood the market, squeezing out human artists and depriving the world of their creativity.

Ludwig van Beethoven died before he completed what would have been his tenth and final symphony. A team of computer scientists and music scholars approximated the music that might have been.

The process known as image-to-image style transfer — mapping, say, the character of a painting’s brushstrokes onto a photo — can render inconsistent results. When they apply the styles of different artists to the same target

The previous state-of-the-art model for playing vintage Atari games took advantage of a number of advances in reinforcement learning (RL). The new champion is a basic RL architecture plus a trick borrowed from image generation.

Creative engineers are combining deep learning systems to produce a groundswell of generated imagery. Researchers, hackers, and artists are producing new works by pairing CLIP, a pretrained image classifier, with a generative adversarial network (GAN).

A recent generative adversarial network (GAN) produced more coherent images using modified transformers that replaced fully connected layers with convolutional layers. A new GAN achieved a similar end using transformers in their original form.

Attention quantifies how each part of one input affects the various parts of another. Researchers added a step that reverses this comparison to produce more convincing images.

A new system combines verbal descriptions and crude lines to visualize complex scenes. Google researchers led by Jing Yu Koh proposed Tag-Retrieve-Compose-Synthesize (TReCS), a system that generates photorealistic images by describing what they want to see while mousing around on a blank screen.