Generative Adversarial Networks

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio · Advances in Neural Information Processing Systems (NeurIPS) · 2014 · artificial-intelligence · record goodfellow2014-gan
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How to read this page. The article itself is not reproduced here — this paper is not cleared for redistribution, so what follows is the tag layer alone. The evidence sentences are withheld with the article. The table at the foot lists every tag the record carries; each row names the facet it belongs to, and the whose words column says whether the tag records the paper's own wording or the tagger's sentence about the paper. Every tag links to each other paper in the corpus carrying it. The machine-readable edition of the same thing is the TEI file beside this one, where the tags live in standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

Other fields define these differently the same word, defined another way in another discipline
training 3 fields · variance 2 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 7 concept tags on this paper, 5 are grounded in a quoted sentence.

generative adversarial network generative AI discriminator minimax game adversarial nets Parzen window log-likelihood adversarial training: a generator and discriminator compete in a minimax game - the generator learns to produce samples that fool the discriminator (which learns real vs fake), driving the generator toward the data distribution generative adversarial generator distribution minimax probability training samples generative adversarial net discriminator D Bayesian standard-error standard error variance system-or-tool-building

Legend

relation to other work	`claim_relations`	how this paper stands to another: agrees, contradicts, extends, qualifies
claim type	`claim_type`	the shape of the claim: existence, mechanism, method, critique, prediction, null
concept edge	`concept_edges`	an asserted relation between two concepts, with the sentence asserting it
concept	`concepts`	an idea the paper works with, quoted where the paper uses it
content tag	`content_tags`	a finding, definition, quantity, statistic or variable named in the paper
control	`controls`	an experiment run to eliminate a rival explanation
limitation	`limitations`	what the paper says its evidence cannot support
mechanism	`mechanisms`	how the reported effect is said to come about
method	`method`	how the paper produced its evidence
phenomenon	`phenomenon`	the effect or regularity under study
sense	`senses`	a distinct use of a term, quoted where the paper fixes it
transfer	`transfers`	a method or concept carried in from another field

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://arxiv.org/abs/1406.2661. The reading view exists for the papers whose licence permits it.

Every tag on this page

claim_relations 5 · claim_type 1 · concept_edges 2 · concepts 5 · content_tags 7 · controls 2 · limitations 10 · mechanisms 1 · method 1 · phenomenon 1 · senses 4 · transfers 1

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 2 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facet	tag	field	conf.	whose words
claim_type	method Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_type`		the paper's words
phenomenon	adversarial training of generative models Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`phenomenon`		the paper's words
method	system-or-tool-building Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: system-or-tool-building	`method`		the paper's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: adversarial training: a generator and discriminator compete in a minimax game - the generator learns to produce samples that fool the discriminator (which learns real vs fake), driving the generator toward the data distribution	`mechanisms[0]`		the tagger's words
limitations	the likelihood-estimation method has high variance and does not perform well in high dimensions Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the reported likelihood numbers: the estimation method DOES NOT PERFORM WELL IN HIGH DIMENSIONAL SPACES	`limitations[0]`		the tagger's words
limitations	The model gives no explicit representation of the generator's density, and the discriminator must be kept synchronised with the generator or the generator collapses (the 'Helvetica scenario'). Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: any use requiring a density or stable training: THERE IS NO EXPLICIT REPRESENTATION OF pg(x), AND D MUST BE SYNCHRONIZED WELL WITH G DURING TRAINING	`limitations[1]`		the tagger's words
limitations	The Parzen-window likelihood estimate used to evaluate the model is high-variance and performs poorly in high dimensions. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the same scope as limitations[0], with the reason it was used anyway: IT HAS SOMEWHAT HIGH VARIANCE AND DOES NOT PERFORM WELL IN HIGH DIMENSIONAL SPACES BUT IT IS THE BEST METHOD AVAILABLE TO OUR KNOWLEDGE	`limitations[2]`		the tagger's words
limitations	The convergence result is proved in the non-parametric limit; using a multilayer perceptron for G breaks the theoretical guarantee. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the convergence guarantee, which is proved in the non-parametric limit and does not cover the implementation: USING A MULTILAYER PERCEPTRON TO DEFINE G INTRODUCES MULTIPLE CRITICAL POINTS IN PARAMETER SPACE	`limitations[3]`		the tagger's words
limitations	The authors do not claim the generated samples are better than those of existing methods. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: any comparison with existing generative models: WHILE WE MAKE NO CLAIM THAT THESE SAMPLES ARE BETTER THAN SAMPLES GENERATED BY EXISTING METHODS, WE BELIEVE THAT THESE SAMPLES ARE AT LEAST COMPETITIVE	`limitations[4]`		the tagger's words
limitations	The authors state their evaluation method is high-variance and poor in high dimensions, call it the best available, and report the paper's headline numbers on it. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the log-likelihood figures in Table 1: they come from Gaussian Parzen-window estimation, which the authors say	`limitations[5]`	1.0	the tagger's words
limitations	The convergence results are proved in the non-parametric limit, and the paper states the implementation does not satisfy that condition and has no theoretical guarantee. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the guarantee that pg converges to pdata: it is proved in a non-parametric setting, while the implementation optimises the parameters of a multilayer perceptron, which the paper says lacks theoretical guarantees	`limitations[6]`	1.0	the tagger's words
limitations	The objective that is trained is not the objective that is analysed; the substitution is made because the analysed one saturates, and the equivalence claimed is of fixed points only. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the relation between Eq. 1 and what was run: G is trained to maximize log D(G(z)) instead, which the paper says gives the same fixed point but much stronger gradients early in learning	`limitations[7]`	1.0	the tagger's words
limitations	The two stated disadvantages are structural, not incidental: the model has no explicit density, and training can collapse to too few outputs if the two networks fall out of step. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: adversarial nets as a density model or as a stable training procedure: there is no explicit representation of pg(x), and D must be synchronized well with G to avoid G collapsing too many values of z to the same value of x	`limitations[8]`	1.0	the tagger's words
limitations	The claim made for the sample quality is a stated belief, and the paper explicitly declines to claim the samples are better than existing methods. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the figures of generated samples as evidence of quality: the authors make no claim that they are better than existing methods and state only a belief that they are at least competitive	`limitations[9]`	1.0	the tagger's words
transfers	a minimax two-player game between a generator and a discriminator, adapting game-theoretic adversarial competition to train generative models Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`transfers[0]`		the tagger's words
controls	the displayed samples declared to be FAIR RANDOM DRAWS rather than selected Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: selection of the best outputs as the basis for judging the model - a figure of hand-picked samples shows what the model CAN do at its best, which is not what the distribution looks like, and nothing in the image itself would reveal the difference · outcome (stated by the tagger, no quotation): held	`controls[0]`	0.95	the tagger's words
controls	a column showing the NEAREST TRAINING EXAMPLE beside each generated sample Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: memorisation of the training set as the explanation of sample quality - a model that reproduced its training data would look excellent by every visual measure, and the neighbour column is what makes that checkable by the reader · outcome (stated by the tagger, no quotation): held	`controls[1]`	0.95	the tagger's words
concepts	discriminator Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: discriminator	`concepts[2]`		the paper's words
concepts	minimax game Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: minimax game	`concepts[3]`		the paper's words
concepts	adversarial nets Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: adversarial nets	`concepts[4]`		the paper's words
concepts	Parzen window Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: Parzen window	`concepts[5]`		the paper's words
concepts	log-likelihood Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: log-likelihood	`concepts[6]`		the paper's words
senses	the training objective - AS ANALYSED vs AS TRAINED: the minimax value function of Eq. 1 is what the theorems are about; the implementation maximises a different function with the same fixed point, because the analysed one saturates early in learning Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[0]`	1.0	the tagger's words
senses	log-likelihood - AS A PARZEN-WINDOW ESTIMATE: not computed from the model, but from a Gaussian fitted to samples drawn from it, because the model has no tractable density Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[1]`	1.0	the tagger's words
senses	adversarial nets - THE PAPER'S OWN NAME, and it names the special case, not the framework: the framework is general and 'adversarial nets' is what the authors call the multilayer-perceptron instance of it Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[2]`	1.0	the tagger's words
senses	the failure mode - NAMED AS A JOKE: 'the Helvetica scenario', in which the generator maps too many inputs to the same output; the field later renamed this mode collapse, and Table 2 records it as the single word Helvetica Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[3]`	1.0	the tagger's words
claim_relations	builds-on → game theory (minimax two-player game) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[0]`		the tagger's words
claim_relations	builds-on → SML/PCD training [31, 29] Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[1]`		the tagger's words
claim_relations	builds-on → the Parzen-window likelihood evaluation procedure of Breuleux et al. [8] Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[2]`		the tagger's words
claim_relations	extends → the generative stochastic network (GSN) framework [5] Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[3]`		the tagger's words
claim_relations	extends → noise-contrastive estimation (NCE) [13] Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[4]`		the tagger's words
concept_edges	log-likelihood —operationalised-as→ Parzen window Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. strength of the relation: 1 also in: log-likelihood · Parzen window	`concept_edges[0]`		the tagger's words
concept_edges	adversarial nets —operationalised-as→ minimax game Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. strength of the relation: 1 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'D and G play the following two-player minimax game', not 'adversarial nets'. also in: adversarial nets · minimax game	`concept_edges[1]`		the tagger's words
content_tags	D = 1/2 — discriminator output at the optimal solution (cannot distinguish real from fake) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`content_tags.quantities[0]`		the tagger's words
content_tags	A unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere; with multilayer perceptrons the whole system can be trained by backpropagation. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`content_tags.findings[1]`		the tagger's words
content_tags	generative adversarial net Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: generative adversarial net	`content_tags.definitions[0]`		the tagger's words
content_tags	discriminator D Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: discriminator D	`content_tags.definitions[1]`		the tagger's words
content_tags	Bayesian Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: Bayesian	`content_tags.statistics[0]`		the tagger's words
content_tags	standard-error Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted. also in: standard-error	`content_tags.statistics[1]`		the tagger's words
content_tags	variance Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: variance	`content_tags.statistics[2]`		the tagger's words

Interpretive choices

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