#GenerativeAdversarialNetworks - Sigmoid Social

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IT News @itnewsbot@schleuss.online

Fake AI law firms are sending fake DMCA threats to generate fake SEO gains - Enlarge / A person made of many parts, similar to the attorney who hand... - https://arstechnica.com/?p=2014933 #generativeadversarialnetworks #artificialintelligence #aigeneration #generativeai #backlinks #dmcaabuse #biz⁢ #tech #dmca #seo #ai

Ars Technica · Apr 4, 2024Fake AI law firms are sending fake DMCA threats to generate fake SEO gainsHow one journalist found himself targeted by generative AI over a keyfob photo.

Fabrizio Musacchio @pixeltracker

I was curious whether it would be possible to let #GANs generate samples conditioned on a specific input type. I wanted the GAN to generate samples of a specific digit, resembling a personal poor man’s mini #DALLE . And indeed, I found a GAN architecture, that allows so-called #ConditionalGANs

https://www.fabriziomusacchio.com/blog/2023-07-30-cgan/

GIF

#MachineLearning #GenerativeAdversarialNetworks

Fabrizio Musacchio @pixeltracker

The #Wasserstein #metric (#EMD) can be used, to train #GenerativeAdversarialNetworks (#GANs) more effectively. This tutorial compares a default GAN with a #WassersteinGAN (#WGAN) trained on the #MNIST dataset.

https://www.fabriziomusacchio.com/blog/2023-07-29-wgan/

GIF

#MachineLearning

Chucky Schuster @chucky_000fff

Milky Way - lactose is everywhere.
Even in our galaxy.
#digitalArt #aiart #newmediaart #digitalArt #midjourney #generativeadversarialnetworks #surrealism #DarkAesthetic #milkyway #ai

J. de Curtò @DeCurto

New preprint: Signature and Log-signature for the Study of Empirical Distributions Generated with GANs #GenerativeAdversarialNetworks #SignatureTransform
https://arxiv.org/abs/2203.03226

arXiv.orgSignature and Log-signature for the Study of Empirical Distributions Generated with GANsIn this paper, we bring forward the use of the recently developed Signature Transform as a way to measure the similarity between image distributions and provide detailed acquaintance and extensive evaluations. We are the first to pioneer RMSE and MAE Signature, along with log-signature as an alternative to measure GAN convergence, a problem that has been extensively studied. We are also forerunners to introduce analytical measures based on statistics to study the goodness of fit of the GAN sample distribution that are both efficient and effective. Current GAN measures involve lots of computation normally done at the GPU and are very time consuming. In contrast, we diminish the computation time to the order of seconds and computation is done at the CPU achieving the same level of goodness. Lastly, a PCA adaptive t-SNE approach, which is novel in this context, is also proposed for data visualization.

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