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  2. Wasserstein GAN - Wikipedia

    en.wikipedia.org/wiki/Wasserstein_GAN

    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".

  3. Generative adversarial network - Wikipedia

    en.wikipedia.org/wiki/Generative_adversarial_network

    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]

  4. Ganimal - Wikipedia

    en.wikipedia.org/wiki/Ganimal

    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.

  5. StyleGAN - Wikipedia

    en.wikipedia.org/wiki/StyleGAN

    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.

  6. Adversarial machine learning - Wikipedia

    en.wikipedia.org/wiki/Adversarial_machine_learning

    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.

  7. Reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning

    Adversarial deep reinforcement learning is an active area of research in reinforcement learning focusing on vulnerabilities of learned policies. In this research area some studies initially showed that reinforcement learning policies are susceptible to imperceptible adversarial manipulations.

  8. SANS Institute - Wikipedia

    en.wikipedia.org/wiki/SANS_Institute

    Free webcasts and email newsletters (@Risk, Newsbites, Ouch!) have been developed in conjunction with security vendors. The actual content behind SANS training courses and training events remains "vendor-agnostic". Vendors cannot pay to offer their own official SANS course, although they can teach a SANS "hosted" event via sponsorship.

  9. Synthetic media - Wikipedia

    en.wikipedia.org/wiki/Synthetic_media

    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 ...