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GANs can be regarded as a case where the environmental reaction is 1 or 0 depending on whether the first network's output is in a given set. [109] Other people had similar ideas but did not develop them similarly. An idea involving adversarial networks was published in a 2010 blog post by Olli Niemitalo. [110]
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".
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.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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.
The Inception Score (IS) is an algorithm used to assess the quality of images created by a generative image model such as a generative adversarial network (GAN). [1] The score is calculated based on the output of a separate, pretrained Inception v3 image classification model applied to a sample of (typically around 30,000) images generated by the generative model.
In March 2020, 15.ai, created by an anonymous MIT researcher, was a free web application that could generate convincing character voices using minimal training data. [42] The platform is credited as the first mainstream service to popularize AI voice cloning ( audio deepfakes ) in memes and content creation , influencing subsequent developments ...
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 ...