​​NVidia released a technology to change face alignment on video

Nvidia has unveiled AI face-alignment that means you're always looking at the camera during video calls. Its new Maxine platform uses GANs to reconstruct the unseen parts of your head — just like a deepfake.

Link: https://www.theverge.com/2020/10/5/21502003/nvidia-ai-videoconferencing-maxine-platform-face-gaze-alignment-gans-compression-resolution

#NVidia #deepfake #GAN

​​StyleGAN2 with adaptive discriminator augmentation (ADA)

Github: https://github.com/NVlabs/stylegan2-ada
ArXiV: https://arxiv.org/abs/2006.06676

#StyleGAN #GAN #DL #CV

​​Tutorial on Generative Adversarial Networks (GANs) with Keras and TensorFlow

Nice tutorial with enough theory to understand what you are doing and code to get it done.

Link: https://www.pyimagesearch.com/2020/11/16/gans-with-keras-and-tensorflow/

#Keras #TensorFlow #tutorial #wheretostart #GAN

​​MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs

Work on conditional image generation

ArXiV: https://arxiv.org/abs/2012.02821

#GAN #DL #food2vec

​​this comic does not exist

horror dataset + stylegan2


pdf book here

#gan #comix #book

​​🔥New breakthrough on text2image generation by #OpenAI

DALL·E: Creating Images from Text

This architecture is capable of understanding style descriptions as well as complex relationship between objects in context.

That opens whole new perspective for digital agencies, potentially threatening stock photo sites and new opportunies for regulations and lawers to work on.

Interesting times!

Website: https://openai.com/blog/dall-e/

#GAN #GPT3 #openai #dalle #DL

​​JigsawGAN: Self-supervised Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks

The authors suggest a GAN-based approach for solving jigsaw puzzles. JigsawGAN is a self-supervised method with a multi-task pipeline: classification branch classifies jigsaw permutations, GAN branch recovers features to images with the correct order.
The proposed method can solve jigsaw puzzles efficiently by utilizing both semantic information and edge information simultaneously.


Paper: https://arxiv.org/abs/2101.07555

#deeplearning #jigsaw #selfsupervised #gan

​​Real-World Super-Resolution of Face-Images from Surveillance Cameras

Most SR methods are trained on LR (low resolution) data, which is downsampled from HR (high resolution) data using bicubic interpolation, but real-life LR images are usually different, so models work worse on them. In this paper, the authors suggest using blur kernels, noise, and JPEG compression artifacts to generate LR images similar to the original ones.
Another suggested improvement is using ESRGAN and replacing VGG-loss with LPIPS-loss, as well as adding PatchGAN.
In addition, the authors show that NIMA metric better correlates with human perception (mean opinion rank) than traditional Image Quality Assessment methods.

Paper: https://arxiv.org/abs/2102.03113

#deeplearning #superresolution #gan #facesuperresolution

Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains

This is an interesting paper about learning and combining representations of object shape and appearance from the different domains (for example, dogs and cars). This allows to create a model, which borrows different properties from each domain and generates images, which don't exist in a single domain.
The main idea is the following:
- use FineGAN as a base model;
- represent object appearance with a differentiable histogram of visual features;
- optimize the generator so that images with different shapes but similar appearances produce similar histograms;

Paper: https://openreview.net/forum?id=M88oFvqp_9
Project link: https://utkarshojha.github.io/inter-domain-gan/
Code will be available here: https://github.com/utkarshojha/inter-domain-gan

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-furrycars

#cv #gan #deeplearning #contrastivelearning

Unsupervised 3D Neural Rendering of Minecraft Worlds

Work on unsupervised neural rendering framework for generating photorealistic images of Minecraft (or any large 3D block worlds).

Why this is cool: this is a step towards better graphics for games.

Project Page: https://nvlabs.github.io/GANcraft/
YouTube: https://www.youtube.com/watch?v=1Hky092CGFQ&t=2s

#GAN #Nvidia #Minecraft

GAN Prior Embedded Network for Blind Face Restoration in the Wild

New proposed method allowed authors to improve the quality of old photoes

ArXiV: https://arxiv.org/abs/2105.06070
Github: https://github.com/yangxy/GPEN

#GAN #GPEN #blind_face_restoration #CV #DL

​​🔥Alias-Free Generative Adversarial Networks (StyleGAN3) release

King is dead! Long live the King! #StyleGAN2 was #SOTA and default standard for generating images. #Nvidia released update version, which will lead to more realistic images generated by the community.

Article: https://nvlabs.github.io/stylegan3/
GitHub: https://github.com/NVlabs/stylegan3
Colab: https://colab.research.google.com/drive/1BXNHZBai-pXtP-ncliouXo_kUiG1Pq7M

#GAN #dl

​​EditGAN: High-Precision Semantic Image Editing

Nvidia researches built an approach for editing segments of a picture with supposedly realtime picture augmentation according to the segment alterations. No demo is available yet though.

All the photoshop power users should relax, because appereance of such a tools means less work for them, not that the demand for the manual retouch will cease.

Website: https://nv-tlabs.github.io/editGAN/
ArXiV: https://arxiv.org/abs/2111.03186

#GAN #Nvidia

🦜 Hi!

We are the first Telegram Data Science channel.

Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.


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Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.

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GLIDE for image augmentation aka ToadVerse technical details

Technical details on how we used GLIDE for image augmentation.

Article Link: https://mirror.xyz/kefirski.eth/XN1cV27uHcAjN_tPSc_ckgSz4B3Nfh5l5HH9lRs9xEE

#GAN #StyleGAN2 #GLIDE #art #art_generation

Imagen — new neural network for picture generation from Google

TLDR: Competitor of DALLE was released.

Imagen — text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. #Google key discovery is that generic large language models (e.g. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model.

Website: https://imagen.research.google

#GAN #CV #DL #Dalle