Title 15, Commerce and TradeRelease 119-110
§9204 Generative Adversarial Network Defined
Title 15 › CHAPTER 117 — IDENTIFYING OUTPUTS OF GENERATIVE ADVERSARIAL NETWORKS › § 9204
Summary
Defines a generative adversarial network as an AI training method with two neural networks that compete. Generator: makes synthetic outputs. Discriminator: tries to spot those outputs. They learn from each other in a feedback loop so both get better.
Full Legal Text
Title 15, §9204
Commerce and Trade, Source: USLM XML via OLRC
In this chapter, the term “generative adversarial network” means, with respect to artificial intelligence, the machine learning process of attempting to cause a generator artificial neural network (referred to in this section as the “generator” and a discriminator artificial neural network (referred to in this section as a “discriminator”) to compete against each other to become more accurate in their function and outputs, through which the generator and discriminator create a feedback loop, causing the generator to produce increasingly higher-quality artificial outputs and the discriminator to increasingly improve in detecting such artificial outputs.
Legislative History
Notes & Related Subsidiaries
Editorial Notes
References in Text
This chapter, referred to in text, was in the original “this Act”, meaning Pub. L. 116–258, Dec. 23, 2020, 134 Stat. 1150, known as the Identifying Outputs of Generative Adversarial Networks Act and also as the IOGAN Act, which is classified principally to this chapter. For complete classification of this Act to the Code, see
Short Title
note set out under section 9201 of this title and Tables. This section, referred to in text, was in the original “this paragraph”, and was translated as reading “this section”, meaning section 6 of Pub. L. 116–258, to reflect the probable intent of Congress.
Reference
Citations & Metadata
Citation
15 U.S.C. § 9204
Title 15, Commerce and Trade
Last Updated
Sep 23, 2026
Release point: 119-110