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
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
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 →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014). — SourceScore Claim 5b0c0612bd9e55b0 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/5b0c0612bd9e55b0.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/5b0c0612bd9e55b0/" width="100%" height="360" frameborder="0" loading="lazy" title="Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014)."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
Sequence-to-Sequence Learning (seq2seq) introduced in paper: Sequence to Sequence Learning with Neural Networks (Sutskever, Vinyals, Le, 2014).
ff80a25ed7e83b45 · 100% confidence · shares 3 tags (foundational, 2014, nips)
Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017).
ad17e76a8baad7a1 · 100% confidence · shares 2 tags (foundational, nips)
Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017).
67866330cd60e54d · 100% confidence · shares 2 tags (foundational, nips)
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).
d15057ced937a103 · 100% confidence · shares 2 tags (foundational, nips)
Direct Preference Optimization (DPO) introduced in paper: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023).
a3e691683a4577af · 100% confidence · shares 2 tags (foundational, nips)
Frequently asked questions
Is the claim "Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014)." verified?
Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014)."?
Evidence comes from 2 primary sources: arXiv (Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio), Wikipedia. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/5b0c0612bd9e55b0.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
How can I cite this SourceScore claim in my code or article?
Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/5b0c0612bd9e55b0.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
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()