Generative Adversarial Network (GAN) Training Algorithm

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An Generative Adversarial Network (GAN) Training Algorithm is a neural network training algorithm in which a generator NNet predicts a sample from the space while the discriminator NNet predicts whether the sample is generated or real.



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  • (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Generative_adversarial_networks#Method Retrieved:2017-6-14.
    • One network is generative and one is discriminative.[1] Typically, the generative network is taught to map from a latent space to a particular data distribution of interest, and the discriminative network is simultaneously taught to discriminate between instances from the true data distribution and synthesized instances produced by the generator. The generative network's training objective is to increase the error rate of the discriminative network (i.e., "fool" the discriminator network by producing novel synthesized instances that appear to have come from the true data distribution). These models are used for computer vision tasks.[1] In practice, a particular dataset serves as the training data for the discriminator. Training the discriminator involves presenting the discriminator with samples from the dataset and samples synthesized by the generator, and backpropagating from a binary classification loss. In order to produce a sample, typically the generator is seeded with a randomized input that is sampled from a predefined latent space (e.g., a multivariate normal distribution). Training the generator involves back-propagating the negation of the binary classification loss of the discriminator. The generator adjusts its parameters so that the training data and generated data cannot be distinguished by the discriminator model. The goal is to find a setting of parameters that makes generated data look like the training data to the discriminator network.[2] In practice, the generator is typically a deconvolutional neural network, and the discriminator is a convolutional neural network. The idea to infer models in a competitive setting (model versus discriminator) was first proposed by Li, Gauci and Gross in 2013.[3] Their method is used for behavioral inference. It is termed Turing Learning,[4] as the setting is akin to that of a Turing test.

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  • https://www.quora.com/What-are-Generative-Adversarial-Networks
    • QUOTE: Generative Adversarial Networks (GANs) are neural networks that are trained in an adversarial manner to generate data mimicking some distribution. To understand this deeply, first you'll have to understand what a generative model is. In machine learning, the two main classes of models are generative and discriminative. A discriminative model is one that discriminates between two (or more) different classes of data - for example a convolutional neural network that is trained to output 1 given an image of a human face and 0 otherwise. A generative model on the other hand doesn't know anything about classes of data. Instead, its purpose is to generate new data which fits the distribution of the training data - for example, a Gaussian Mixture Model is a generative model which, after trained on a set of points, is able to generate new random points which more-or-less fit the distribution of the training data (assuming a GMM is able to mimic the data well). More specifically, a generative model g trained on training data X sampled from some true distribution D is one which, given some standard random distribution Z, produces a distribution D′ which is close to D according to some closeness metric (a sample z∼Z maps to a sample g(z)∼D′).

      The 'standard' way to determine a generative model g given training data X is maximum-likelihood, which requires all sorts of calculations of marginal probabilities, partition functions, most-likely estimates, etc. This may be feasible when your generative model is a GMM, but if you want to try to make a generative model out of a deep neural network, this quickly becomes intractable.

      Adversarial training allows you to train a generative model without all of these intractable calculations. Let's assume our training data X⊂Rd . The basic idea is that you will have two adversarial models - a generator g:Rn→Rd and a discriminator d:Rd→{0,1}. The generator will be tasked with taking in a given sample from a standard random distribution (e.g. a sample from an n-dimensional Gaussian) and producing a point that looks sort of like it could come from the same distribution as X. The discriminator, on the other hand, will be tasked with discriminating between samples from the true data X and the artificial data generated by g. Each model is trying to best the other - the generator's objective is to fool the discriminator and the discriminator's objective is to not be fooled by the generator.

      In our case, both g and d are neural nets. And what happens is that we train them both in an alternating manner. Each of their objectives can be expressed as a loss function that we can optimize via gradient descent. So we train g for a couple steps, then train d for a couple steps, then give g the chance to improve itself, and so on. The result is that the generator and the discriminator each get better at their objectives in tandem, so that at the end, the generator is able to or is close to being able to fool the most sophisticated discriminator. In practice, this method ends up with generative neural nets that are incredibly good at producing new data (e.g. random pictures of human faces).

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  • (Lysyanskaya, et al., 2014) ⇒ Anna Lysyanskaya, Roberto Tamassia, and Nikos Triandopoulos. "Multicast authentication in fully adversarial networks.” In: Security and Privacy, 2004. Proceedings. 2004 IEEE Symposium on, pp. 241-253. IEEE, 2004.

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