Search results
Results from the WOW.Com Content Network
Transformer GAN (TransGAN): [31] Uses the pure transformer architecture for both the generator and discriminator, entirely devoid of convolution-deconvolution layers. Flow-GAN: [32] Uses flow-based generative model for the generator, allowing efficient computation of the likelihood function.
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 : [,].
Repeating this process, where each new model is trained on the previous model's output, leads to progressive degradation and eventually results in a "model collapse" after multiple iterations. [186] Tests have been conducted with pattern recognition of handwritten letters and with pictures of human faces. [ 187 ]
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
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.
a generative model is a model of the conditional probability of the observable X, given a target y, symbolically, (=) [2] a discriminative model is a model of the conditional probability of the target Y , given an observation x , symbolically, P ( Y ∣ X = x ) {\displaystyle P(Y\mid X=x)} [ 3 ]
If you love Scrabble, you'll love the wonderful word game fun of Just Words. Play Just Words free online!
The Fréchet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial network (GAN) [1] or a diffusion model. [2] [3] The FID compares the distribution of generated images with the distribution of a set of real images (a "ground truth" set).