Verified claim · AI-ML · 82% confidence
DenseNet (Densely Connected Convolutional Networks) introduced in paper: Densely Connected Convolutional Networks (Huang et al., 2016).
Last verified 2026-06-02 · Methodology veritas-v0.1 · b93bddc35c1a22a6
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Structured fields
- Subject
- DenseNet (Densely Connected Convolutional Networks)
- Predicate
introduced_in_paper- Object
- Densely Connected Convolutional Networks (Huang et al., 2016)
- Confidence
- 82%
- Tags
- densenet · convolutional-network · feature-reuse · huang · weinberger · foundational · 2016
Sources (2)
[1] preprint · arXiv (Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger) · 2016-08-25
Densely Connected Convolutional Networks“In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections.”
[2] docs · Hugging Face
Densely Connected Convolutional Networks (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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Yes — SourceScore verified this claim with 82% confidence as of 2026-06-02. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
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Evidence comes from 2 primary sources: arXiv (Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger), Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/b93bddc35c1a22a6.json includes an HMAC-SHA256 signature for audit verification.
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// "DenseNet (Densely Connected Convolutional Networks) introduced in paper: Densely Connected Convolutional Networks (Huang et al., 2016)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/b93bddc35c1a22a6.json")
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# "DenseNet (Densely Connected Convolutional Networks) introduced in paper: Densely Connected Convolutional Networks (Huang et al., 2016)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_densenet_densely_connected_convolutional_networks_fact() -> dict:
"""Fetch the verified SourceScore claim for DenseNet (Densely Connected Convolutional Networks)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/b93bddc35c1a22a6.json")
return r.json()