What is recorded here?
Introduced mobile and embedded vision networks built with depthwise-separable convolutions and size/latency trade-off controls.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The original MobileNet paper is not the invention of depthwise-separable convolutions and is distinct from later MobileNetV2/V3 architectures.
Read the original work
- Howard and colleagues: MobileNet efficient on-device vision models — original paper
- Howard and colleagues: MobileNet efficient on-device vision models — company research source
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Andrew G. Howard; Menglong Zhu; Bo Chen; Dmitry Kalenichenko; Weijun Wang; Tobias Weyand; Marco Andreetto; Hartwig Adam
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.
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English catalogue narrative
Howard and colleagues: MobileNet efficient on-device vision models
Introduced mobile and embedded vision networks built with depthwise-separable convolutions and size/latency trade-off controls.
Scientific scope
The original MobileNet paper is not the invention of depthwise-separable convolutions and is distinct from later MobileNetV2/V3 architectures.
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
Andrew G. Howard; Menglong Zhu; Bo Chen; Dmitry Kalenichenko; Weijun Wang; Tobias Weyand; Marco Andreetto; Hartwig Adam
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.