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Concretely, the conditional GAN game is just the GAN game with class labels provided: (,):=, [ (,)] +, [ ((,))] where is a probability distribution over classes, () is the probability distribution of real images of class , and () the probability distribution of images generated by the generator when given class label .
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.
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).
To approximate the conditional log-likelihood a model seeks to maximize, the hierarchical softmax method uses a Huffman tree to reduce calculation. The negative sampling method, on the other hand, approaches the maximization problem by minimizing the log-likelihood of sampled negative instances.
A 2019 paper proposed the noise conditional score network (NCSN) or score-matching with Langevin dynamics (SMLD). [7] The paper was accompanied by a software package written in PyTorch release on GitHub. [8] A 2020 paper [9] proposed the Denoising Diffusion Probabilistic Model (DDPM), which improves upon the previous method by variational ...
The conditional VAE (CVAE), inserts label information in the latent space to force a deterministic constrained representation of the learned data. [ 15 ] Some structures directly deal with the quality of the generated samples [ 16 ] [ 17 ] or implement more than one latent space to further improve the representation learning.
Discriminative models, also referred to as conditional models, are a class of models frequently used for classification.They are typically used to solve binary classification problems, i.e. assign labels, such as pass/fail, win/lose, alive/dead or healthy/sick, to existing datapoints.
This rule allows one to express a joint probability in terms of only conditional probabilities. [4] The rule is notably used in the context of discrete stochastic processes and in applications, e.g. the study of Bayesian networks, which describe a probability distribution in terms of conditional probabilities.