Images generated by a network designed to visualize what goes on in peoples’ brains while they watch Doctor Who
Generative Adversarial Network (GAN)

What the Brain Sees: AI uses brain activity to create images.

What’s creepier than images from the sci-fi TV series Doctor Who? Images generated by a network designed to visualize what goes on in peoples’ brains while they watch Doctor Who.
Detection of a digitally altered image of a frog holding a violin
Generative Adversarial Network (GAN)

Fighting Fakes: Six algorithms that help news sites spot deepfakes

A supergroup of machine learning models flags manipulated photos. Jigsaw, a tech incubator owned by Alphabet, released a system that detects digitally altered images.
Examples of images being produced from noise
Generative Adversarial Network (GAN)

Images From Noise: An upgrade for score-based generative AI models.

Generative adversarial networks and souped-up language models aren’t the only image generators around. Researchers recently upgraded an alternative known as score-based generative models.
Data related to adversarial learning
Generative Adversarial Network (GAN)

Adversarial Helper: Adversarial learning can improve vision and NLP.

Models that learn relationships between images and words are gaining a higher profile. New research shows that adversarial learning, usually a way to make models robust to deliberately misleading inputs, can boost vision-and-language performance.
AI-generated images with the model DALL-E
Generative Adversarial Network (GAN)

Tell Me a Picture: OpenAI's two new multimodal AI models, CLIP and DALL·E

Two new models show a surprisingly sharp sense of the relationship between words and images. OpenAI, the for-profit research lab, announced a pair of models that have produced impressive results in multimodal learning: DALL·E.
Dozens of snowmen with different characteristics
Generative Adversarial Network (GAN)

This Snowman Does Not Exist: The rise of deepfakes in 2020

While generative adversarial networks were infiltrating cultural, social, and scientific spheres, they quietly transformed the web into a bottomless well of synthetic images of . . . well, you name it.
Examples of Xpression app working (people in pajamas looking like they're wearing suits)
Generative Adversarial Network (GAN)

GAN Makes Pajamas Safe For Work: An AI app allows users to dress in digital costumes.

A new camera app uses a generative adversarial network to let users look like they’re dressed for success while they videoconference in their jammies. Xpression is an iPhone app that maps facial expressions onto till images in real time, allowing users to look clothed in digital costumes.
Graphs showing how DeepRhythm detects deepfakes
Generative Adversarial Network (GAN)

Deepfakes Are Heartless: AI detects deepfaked videos by their lack of heartbeat.

The incessant rhythm of a heartbeat could be the key to distinguishing real videos from deepfakes. DeepRhythm detects deepfakes using an approach inspired by the science of measuring minute changes on the skin’s surface due to blood circulation.
Graphs and examples of Network dissection technique
Generative Adversarial Network (GAN)

What One Neuron Knows: How convolutional neural network layers recognize objects.

How does a convolutional neural network recognize a photo of a ski resort? New research shows that it bases its classification on specific neurons that recognize snow, mountains, trees, and houses.
Series of images showing how Maxine, a media streaming platform, works
Generative Adversarial Network (GAN)

Data Compression By AI: Nvidia uses AI to enhance video conferencing quality.

In this work-from-home era, who hasn’t spent a video conference wishing they could read an onscreen document without turning their eyes from the person they’re talking with? Or simply hoping the stream wouldn’t stutter or stall? Deep learning can fill in the missing pieces.
Examples of AI generated images
Generative Adversarial Network (GAN)

GANs for Smaller Data: Training GANs on small data without overfitting

Trained on a small dataset, generative adversarial networks (GANs) tend to generate either replicas of the training data or noisy output. A new method spurs them to produce satisfying variations.
Excerpts of HBO documentary "Welcome to Chechnya"
Generative Adversarial Network (GAN)

Protected By Deepfakes: Documentary uses deepfakes to protect its sources.

Documentary filmmakers often shield the identities of people who might be harmed for speaking out. But typical tactics like blurring faces and distorting voices can make it hard for audiences to connect emotionally. A new
Data and examples related to a new technique to detect portions of an image
Generative Adversarial Network (GAN)

The Telltale Artifact: A technique for detecting GAN-generated deepfakes

Deepfakes have gone mainstream, allowing celebrities to star in commercials without setting foot in a film studio. A new method helps determine whether such endorsements — and other images produced by generative adversarial networks — are authentic.
Makeup applied on female faces with the help of augmented reality
Generative Adversarial Network (GAN)

A Good Look for AI: How L’Oreal uses AI to power its virtual makeup try-on

Trying on new makeup is a hassle — apply, inspect, wash off, repeat. Not to mention the tribulation of visiting brick-and-mortar stores during the pandemic. Augmented reality is helping people try on all the makeup they want without leaving home.
Data and examples related to IMLE-GAN
Generative Adversarial Network (GAN)

Making GANs More Inclusive: A technique to help GANs represent their datasets fairly

A typical GAN’s output doesn’t necessarily reflect the data distribution of its training set. Instead, GANs are prone to modeling the majority of the training distribution, sometimes ignoring rare attributes — say, faces that represent minority populations.

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