Andrew Howard

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Google

Senior Software Engineer at Google

SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection

Deep learning based object detectors are commonly deployed on mobile dev...
01/04/2021 ∙ by Keren Ye, et al. ∙ 0 ∙ share

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Large-Scale Generative Data-Free Distillation

Knowledge distillation is one of the most popular and effective techniqu...
12/10/2020 ∙ by Liangchen Luo, et al. ∙ 4 ∙ share

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Discovering Multi-Hardware Mobile Models via Architecture Search

Developing efficient models for mobile phones or other on-device deploym...
08/18/2020 ∙ by Grace Chu, et al. ∙ 0 ∙ share

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Non-discriminative data or weak model? On the relative importance of data and model resolution

We explore the question of how the resolution of the input image ("input...
09/07/2019 ∙ by Mark Sandler, et al. ∙ 3 ∙ share

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Visual Wake Words Dataset

The emergence of Internet of Things (IoT) applications requires intellig...
06/12/2019 ∙ by Aakanksha Chowdhery, et al. ∙ 0 ∙ share

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Geo-Aware Networks for Fine Grained Recognition

Fine grained recognition distinguishes among categories with subtle visu...
06/04/2019 ∙ by Grace Chu, et al. ∙ 2 ∙ share

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Searching for MobileNetV3

We present the next generation of MobileNets based on a combination of c...
05/06/2019 ∙ by Andrew Howard, et al. ∙ 53 ∙ share

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Low-Power Computer Vision: Status, Challenges, Opportunities

Computer vision has achieved impressive progress in recent years. Meanwh...
04/15/2019 ∙ by Sergei Alyamkin, et al. ∙ 16 ∙ share

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K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning

We introduce a novel method that enables parameter-efficient transfer an...
10/25/2018 ∙ by Pramod Kaushik Mudrakarta, et al. ∙ 0 ∙ share

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2018 Low-Power Image Recognition Challenge

The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcompu...
10/03/2018 ∙ by Sergei Alyamkin, et al. ∙ 6 ∙ share

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Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning

Transferring the knowledge learned from large scale datasets (e.g., Imag...
06/16/2018 ∙ by Yin Cui, et al. ∙ 0 ∙ share

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NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

This work proposes an automated algorithm, called NetAdapt, that adapts ...
04/09/2018 ∙ by Tien-Ju Yang, et al. ∙ 0 ∙ share

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Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

In this paper we describe a new mobile architecture, MobileNetV2, that i...
01/13/2018 ∙ by Mark Sandler, et al. ∙ 0 ∙ share

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Inverted Residuals and Linear Bottlenecks: Mobile Networks forClassification, Detection and Segmentation

In this paper we describe a new mobile architecture, MobileNetV2, that i...
01/13/2018 ∙ by Mark Sandler, et al. ∙ 0 ∙ share

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Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

The rising popularity of intelligent mobile devices and the daunting com...
12/15/2017 ∙ by Benoit Jacob, et al. ∙ 0 ∙ share

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The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

Current approaches for fine-grained recognition do the following: First,...
11/20/2015 ∙ by Jonathan Krause, et al. ∙ 0 ∙ share

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Dynamical Systems Trees

We propose dynamical systems trees (DSTs) as a flexible class of models ...
07/11/2012 ∙ by Andrew Howard, et al. ∙ 0 ∙ share

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Andrew Howard

Featured Co-authors

Li Fei-Fei

Xin Liu

Quoc V. Le

Yiran Chen

Serge Belongie

Xiao Zhang

Bo Chen

Yang Song

Jonathon Shlens

Chen Feng

Chen Sun

SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection

Large-Scale Generative Data-Free Distillation

Discovering Multi-Hardware Mobile Models via Architecture Search

Non-discriminative data or weak model? On the relative importance of data and model resolution

Visual Wake Words Dataset

Geo-Aware Networks for Fine Grained Recognition

Searching for MobileNetV3

Low-Power Computer Vision: Status, Challenges, Opportunities

K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning

2018 Low-Power Image Recognition Challenge

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

Inverted Residuals and Linear Bottlenecks: Mobile Networks forClassification, Detection and Segmentation

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

Dynamical Systems Trees