24 octobre 2019 / noflux / Commentaires fermés sur Instagram a banni les filtres effet « chirurgie esthétique »
“Un surnom a été donné au fait de se penser imparfait ou imparfaite à cause de filtres. C’est la « dysmorphie Snapchat ». On accuse les filtres de renforcer la pression notamment sur les femmes. Des chirurgiens racontent que de plus en plus d’utilisatrices de réseaux sociaux demandent à se faire opérer pour ressembler à la version « filtrée ». On ignore encore si des filtres vus comme « embellissants » comme celui (très populaire) de l’influenceuse Kylie Jenner seront concernés.”
23 octobre 2019 / noflux / Commentaires fermés sur GitHub – minimaxir/optillusion-animation: Python code to submit rotated images to the Cloud Vision API + R code for visualizing it
Wow, this is cool: The duck-rabbit illusion apparently works on Google Cloud Vision. The system interprets it one way or the other, depending on the orientation of the image. pic.twitter.com/IoiftlRNrQ
“We are building the next generation of media through the power of AI. Copyrights, distribution rights, and infringement claims will soon be things of the past. To give you a glimpse of what we have been working on we created a free resource of 100k high-quality faces. Every image was generated by our internal AI systems as it continually improves. Use them in your presentations, projects, mockups or wherever — all for just a link back to us!”
18 décembre 2018 / noflux / Commentaires fermés sur A Style-Based Generator Architecture for Generative Adversarial Networks
“We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.”
18 octobre 2018 / noflux / Commentaires fermés sur Salmon Farmers Are Scanning Fish Faces to Fight Killer Lice
“In a world where surveillance technology is being deployed everywhere from airports and stadiums to public schools and hotels and raising a plethora of privacy concerns, it’s perhaps inevitable that farms on land and at sea would find ways to exploit it to improve productivity. Just this year, American agribusiness giant Cargill Inc. said it was working with an Irish tech start-up on a facial-recognition system to monitor cows so farmers can adjust feeding regimens to enhance milk production. Scanners will allow them to track food and water intake and even detect when females are having fertile days. Salmon farming may be next in line. As fish vies with beef and chicken as the global protein food of choice, exporters like Norway, the world’s biggest producer of the pinkish-orange fish, have become the focal point for radical marine-farming methods designed to help the $232 billion aquaculture industry feed the world.”
24 juin 2018 / noflux / Commentaires fermés sur Nvidia uses artificial intelligence to fake realistic slow-motion video
“Creating slow-motion footage is all about capturing a large number of frames per second. If you don’t record enough, it becomes choppy and unwatchable as soon as you slow down your video — unless, that is, you use artificial intelligence to imagine the extra frames”.
«We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class. These adversarial patches can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class».