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Quoc V. Le
Researcher at Google
Publications - 61
Citations - 16438
Quoc V. Le is an academic researcher from Google. The author has contributed to research in topics: Object detection & Convolutional neural network. The author has an hindex of 29, co-authored 61 publications receiving 8244 citations.
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Searching for MobileNetV3.
Andrew Howard,Mark Sandler,Grace Chu,Liang-Chieh Chen,Bo Chen,Mingxing Tan,Weijun Wang,Yukun Zhu,Ruoming Pang,Vijay K. Vasudevan,Quoc V. Le,Hartwig Adam +11 more
TL;DR: This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art of MobileNets.
Proceedings ArticleDOI
Searching for MobileNetV3
Andrew Howard,Ruoming Pang,Hartwig Adam,Quoc V. Le,Mark Sandler,Bo Chen,Weijun Wang,Liang-Chieh Chen,Mingxing Tan,Grace Chu,Vijay K. Vasudevan,Yukun Zhu +11 more
TL;DR: MobileNetV3 as mentioned in this paper is the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design and achieves state-of-the-art results for mobile classification, detection and segmentation.
Proceedings ArticleDOI
MnasNet: Platform-Aware Neural Architecture Search for Mobile
TL;DR: In this article, the authors propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.
Proceedings ArticleDOI
Self-Training With Noisy Student Improves ImageNet Classification
TL;DR: A simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images.
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Unsupervised Data Augmentation for Consistency Training
TL;DR: A new perspective on how to effectively noise unlabeled examples is presented and it is argued that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.