Unverified50% confidenceFactExact time
The 'depth' in deep learning refers to a network containing multiple hidden layers.
1
Sources
50%
Confidence
Long-term
Relevance
8/31/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLow-level features such as edges and textures learned by the shallow layers of deep learning models exhibit strong domain-agnostic properties, while high-level semantic features encoded by deep layers are more domain-specific.80% similarVerifiedDeep learning uses multi-layer neural networks whose structure is inspired by the way neurons in the human brain are connected.77% similarUnverified2014 年 Yosinski 等人在论文《How transferable are features in deep neural networks?》中证明网络浅层学习的特征具有极高通用性，而深层特征逐渐变得任务特定75% similarUnverifiedSPLADE and DeepImpact are representatives of learned sparse retrieval, producing sparse vectors whose per-term weights are learned by a neural network and enabling term expansion.75% similarUnverified《Dive into Deep Learning》是覆盖深度学习核心内容的开源教材75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/828240API
curl https://kongchang.com/api/v1/knowledge/claims/828240MCP
get_claim(id=828240)