What is recorded here?
Proposed compound scaling that jointly balances convolutional-network depth, width and input resolution, then used architecture search to build the EfficientNet model family.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Efficiency and accuracy comparisons are author-reported for specified datasets and hardware; compound scaling is not a claim that EfficientNet dominates all later vision models.
Read the original work
- Tan and Le: EfficientNet compound scaling for convolutional networks — original paper
- Tan and Le: EfficientNet compound scaling for convolutional networks — company research page
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Mingxing Tan; Quoc V. Le
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Tan and Le: EfficientNet compound scaling for convolutional networks
Proposed compound scaling that jointly balances convolutional-network depth, width and input resolution, then used architecture search to build the EfficientNet model family.
Scientific scope
Efficiency and accuracy comparisons are author-reported for specified datasets and hardware; compound scaling is not a claim that EfficientNet dominates all later vision models.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Mingxing Tan; Quoc V. Le
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.