<p>Okay, in <a href="https://arxiv.org/pdf/1610.03483.pdf" rel="nofollow noreferrer">this paper</a>, it says:</p>
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<h2>&quot;Prescribed probabilistic models are those that provide an explicit parametric specification of the distribution of an observed random variable <strong>x</strong> [...]. Most models in machine learning and statistics are of this form, whether they be state-of-the-art classifiers for object recognition, complex sequence models for machine translation, or fine-grained spatio-temporal models tracking the spread of disease. Alternatively, we can specify implicit probabilistic models that define a stochastic procedure that directly generates data.&quot;</h2>
<p>Maybe to wrap it up: A <em>prescribed</em> probabilistic model wants to learn a set of parameters suitable for the task and data at hand, e.g. object classification. On the other hand, an <em>implicit</em> probabilistic model generates data, though to be fair, GANs are also fed with data, e.g. MNIST samples, and if properly tuned, they produce MNIST numbers themselves.</p>
<p>Hope that helps a bit!</p>
<p>PS: I'm not sure whether <em>explicit</em> generative networks exists at all. Are you sure, maybe you can send a reference? After all, one always wants to generate data with a GAN ...</p>
Why GAN's (Generative Adverserial networks) are called "implicit" generative networks?
<p>Adverserial Networks, such as GAN's, are called <strong>&quot;implicit&quot;</strong> networks. What does it mean? And, how do they differ from <strong>&quot;explicit&quot;</strong> generative networks? And what are <strong>&quot;explicit&quot;</strong> generative networks?</p>