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  2. Generative adversarial network - Wikipedia

    en.wikipedia.org/wiki/Generative_adversarial_network

    A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence.The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [1]

  3. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    TensorFlow includes an “eager execution” mode, which means that operations are evaluated immediately as opposed to being added to a computational graph which is executed later. [35] Code executed eagerly can be examined step-by step-through a debugger, since data is augmented at each line of code rather than later in a computational graph. [35]

  4. Wasserstein GAN - Wikipedia

    en.wikipedia.org/wiki/Wasserstein_GAN

    The original GAN method is based on the GAN game, a zero-sum game with 2 players: generator and discriminator. The game is defined over a probability space (,,), The generator's strategy set is the set of all probability measures on (,), and the discriminator's strategy set is the set of measurable functions : [,].

  5. StyleGAN - Wikipedia

    en.wikipedia.org/wiki/StyleGAN

    A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. [9]In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces.

  6. Vision transformer - Wikipedia

    en.wikipedia.org/wiki/Vision_transformer

    Vision Transformer architecture, showing the encoder-only Transformer blocks inside. The basic architecture, used by the original 2020 paper, [1] is as follows. In summary, it is a BERT-like encoder-only Transformer.

  7. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    A common technique is to train the network on a larger data set from a related domain. Once the network parameters have converged an additional training step is performed using the in-domain data to fine-tune the network weights, this is known as transfer learning. Furthermore, this technique allows convolutional network architectures to ...

  8. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    The GAN principle was originally published in 1991 by Jürgen Schmidhuber who called it "artificial curiosity": two neural networks contest with each other in the form of a zero-sum game, where one network's gain is the other network's loss. [95] [96] The first network is a generative model that models a probability distribution over output ...

  9. Feedforward neural network - Wikipedia

    en.wikipedia.org/wiki/Feedforward_neural_network

    The two historically common activation functions are both sigmoids, and are described by = ⁡ = (+).The first is a hyperbolic tangent that ranges from -1 to 1, while the other is the logistic function, which is similar in shape but ranges from 0 to 1.