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For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning , GANs have also proved useful for semi-supervised learning , [ 2 ] fully supervised ...
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.
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).
Text (CSV) and TensorFlow Record files Classification 2017 [148] J. Gemmeke et al., Google Bird Audio Detection challenge Audio from environmental monitoring stations, plus crowdsourced recordings 17,000+ Classification 2016 (2018) [149] [150] Queen Mary University and IEEE Signal Processing Society: WSJ0 Hipster Ambient Mixtures
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
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.
In machine learning, the term tensor informally refers to two different concepts (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector ...
In addition to the solutions for errors with smallest squares, margin maximization criteria, so-called training support vector machines, are used to determine the output values. [12] Other variants of echo state networks seek to change the formulation to better match common models of physical systems, such as those typically those defined by ...