Verified claim · AI-ML · 100% confidence
Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 5b0c0612bd9e55b0
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Structured fields
- Subject
- Generative Adversarial Networks (GANs)
- Predicate
introduced_in_paper- Object
- Generative Adversarial Networks (Goodfellow et al., 2014)
- Confidence
- 100%
- Tags
- gan · foundational · goodfellow · 2014 · nips · generative
Sources (2)
[1] preprint · arXiv (Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio) · 2014-06-10
Generative Adversarial Nets“We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G.”
[2] docs · Wikipedia · 2014-12-08
Generative adversarial networkWikipedia is rated by SourceScore — see its reliability →
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curl https://sourcescore.org/api/v1/claims/5b0c0612bd9e55b0.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/5b0c0612bd9e55b0.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/5b0c0612bd9e55b0.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_generative_adversarial_networks_gans_fact() -> dict:
"""Fetch the verified SourceScore claim for Generative Adversarial Networks (GANs)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/5b0c0612bd9e55b0.json")
return r.json()