11 citations · 22 across the 4 of their papers we have counts for
5 papers
Multi-path Neural Networks for On-device Multi-domain Visual Classification
Qifei Wang, Junjie Ke, Joshua Greaves +9
Learning multiple domains/tasks with a single model is important for improving data efficiency and lowering inference cost for numerous vision tasks, especially on resource-constra…
Can weight sharing outperform random architecture search? An investigation with TuNAS
Gabriel Bender, Hanxiao Liu, Bo Chen +4
Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models. There is, howev…
Discovering Multi-Hardware Mobile Models via Architecture Search
Grace Chu, Okan Arikan, Gabriel Bender +7
Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another impor…
Geo-Aware Networks for Fine-Grained Recognition
Grace Chu, Brian Potetz, Weijun Wang +5
Fine-grained recognition distinguishes among categories with subtle visual differences. In order to differentiate between these challenging visual categories, it is helpful to leve…
Searching for MobileNetV3
Andrew Howard, Mark Sandler, Grace Chu +9
We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone…