AI Technology & Industry Review
A team from Google Research presents a neural video compression method based on generative adversarial networks (GANs) that outperforms previous neural video compression methods and advances generative video compression research.
Synced invited Dr. Linchao Zhu, a lecturer at the ReLER lab, University of Technology Sydney whose works focus on video representation learning, to share his thoughts on the paper Text-to-Image Generation Grounded by Fine-Grained User Attention.
SlimGANs can easily change model sizes during runtime to implement quality-efficiency trade-offs based on practical needs.
Researchers propose a neuro-symbolic hybrid approach to address the challenge of creativity in generative art.
GameGAN, a generative model that learns to visually imitate video game environments by ingesting screenplay and keyboard actions during training.
South Korea’s Naver Clova AI Research is one of the institutions behind the unsupervised generative network U-GAT-IT. The tech hasContinue Reading
Researchers have proposed a new and inexpensive method for automatically generating yuru-chara characters.
A research team from MIT, Adobe Research, and Shanghai Jiao Tong University have introduced a novel method for reducing the cost and size of Conditional GAN generators.
Researchers have proposed a new image generative model that leverages the hierarchical space of deep features learned by pretrained classification networks and provides a unified and versatile framework for image generation and manipulation tasks.
The model outperforms existing methods in image manipulation and offers researchers a possible solution to the scarcity of paired datasets.
UC Berkeley and Adobe Research have introduced a “universal” detector that can distinguish real images from generated images regardless of what architectures and/or datasets were used for training.
Proposed by researchers from the Rutgers University and Samsung AI Center in the UK, CookGAN uses an attention-based ingredients-image association model to condition a generative neural network tasked with synthesizing meal images.
In a bid to raise awareness of the threats posed by climate change, the Mila team recently published a paper that uses GANs to generate images of how climate events may impact our environments — with a particular focus on floods.
Researchers have proposed a novel self-adversarial learning (SAL) paradigm for improving GANs’ performance in text generation.
Researchers have proposed a novel generator network specialized on the illustrations in children’s books.
Researchers have introduced the first unsupervised learning approach for identifying interpretable semantic directions in the latent space of generative adversarial network (GAN) models.
Researchers introduced semantic region-adaptive normalization (SEAN), a simple but effective building block for conditional Generative Adversarial Networks (cGAN).
Every Friday Synced selects seven recent studies that present topical, innovative or otherwise interesting or important research we believe may be of interest to our readers.
Researchers from the South Korea-based tech company Hyperconnect recently proposed a new framework, MarioNETte, which aims to reenact target faces in a few-shot manner (from even a single image) while preserving identity without any fine-tuning.
For decades machines have been able to understand simple musical features like beats per minute. Now AI is boosting their abilities to the point that they can not only figure out what particular genre of music is playing, but also how to appropriately dance to it.
Synced Global AI Weekly October 20th
A team of researchers has proposed a Generative Adversarial Network based model tasked with creating playable and aesthetically appealing game levels for popular action-adventure video game series The Legend of Zelda.
DeepMind researchers are expanding GANs to audio, with a new adversarial network approach for high fidelity speech synthesis.
A multi-institute research group recently released the paper PSGAN: Pose-Robust Spatial-Aware GAN for Customizable Makeup Transfer, which proposes a novel method for transferring makeup styles from a reference picture to a user’s source image.
Thanks to the creation of AutoML — which is essentially automated neural architecture search (NAS) — AI can now design better deep neural networks than human researchers for computer vision tasks such as image classification and object detection.
Let’s say you’ve just sketched a portrait but don’t have the inclination to hand-colour your creation. Wouldn’t it be nice if you could simply tell an artificial intelligence assistant what colours you want and have it do the rest? Well, now you can!
Last month a team of South Korean researchers proposed a method that uses unsupervised image translation to transform a simple selfie into a classic Japanese-style anime face.
Do you dream of Asuna Yuuki? Do you long to escape to a fantasy world with a beautiful anime partner? If so there’s a new artificial intelligence system just for you — the “Waifu Vending Machine” can create a highly customized anime companion in minutes.
Leading UK AI research company DeepMind has now proposed “BigBiGAN” (not BigBigGAN, but BigBiGAN), which achieves state-of-the-art results in both unsupervised representation learning on ImageNet and in unconditional image generation.
Generative adversarial networks (GANs) have become AI researchers’ “go-to” technique for generating photo-realistic synthetic images. Now, DeepMind researchers say that there may be a better option.
Compressed Sensing’s ability to exploit the structure of natural images and recover an image from limited random measurements have made it an elegant framework for recovering sparse signals from compressed measurements.
NVIDIA has opened a fun online AI platform (https://nvlabs.github.io/FUNIT/petswap.html) that can swap pet faces onto other animals. Simply upload a photo of your Spot or Sylvester, draw a rectangle around its head, click on “Translate” and voila!
It’s a fanciful little one-piece in shimmering green and aquamarine with bold fuschia shoulder accents — perfect for a night out on the town. Is this a new dress from a Milan or Tokyo collection? Nope, it was designed by an AI-powered machine, and produced by a couple of MIT graduates.
Synced spoke with AI pioneer Professor Yoshua Bengio at the Computing in the 21st Century Conference in Beijing, where he discussed his recent research and the current state of AI.