Search results
Results from the WOW.Com Content Network
Another inspiration for GANs was noise-contrastive estimation, [113] which uses the same loss function as GANs and which Goodfellow studied during his PhD in 2010–2014. Adversarial machine learning has other uses besides generative modeling and can be applied to models other than neural networks.
The Wasserstein Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches".
The Style Generative Adversarial Network, or StyleGAN for short, is an extension to the GAN architecture introduced by Nvidia researchers in December 2018, [1] and made source available in February 2019.
A ganimal, also commonly referred to as GANimal, is a hybrid animal created with generative artificial intelligence systems, such as generative adversarial networks (GANs) or diffusion models. [ 2 ] [ 3 ] [ 4 ] The concept was created for a website from the MIT Media Lab in 2020, where users could create ganimal images.
A generative AI system is constructed by applying unsupervised machine learning (invoking for instance neural network architectures such as generative adversarial networks (GANs), variation autoencoders (VAEs), transformers, or self-supervised machine learning trained on a dataset.
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. [1] A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications.
Synthetic media (also known as AI-generated media, [1] [2] media produced by generative AI, [3] personalized media, personalized content, [4] and colloquially as deepfakes [5]) is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the use of artificial intelligence algorithms, such as for the purpose of ...
For the image generation step, conditional generative adversarial networks (GANs) have been commonly used, with diffusion models also becoming a popular option in recent years. Rather than directly training a model to output a high-resolution image conditioned on a text embedding, a popular technique is to train a model to generate low ...