Search Results for author: Gabriel Bender

Found 9 papers, 5 papers with code

Multi-path Neural Networks for On-device Multi-domain Visual Classification

no code implementations10 Oct 2020 Qifei Wang, Junjie Ke, Joshua Greaves, Grace Chu, Gabriel Bender, Luciano Sbaiz, Alec Go, Andrew Howard, Feng Yang, Ming-Hsuan Yang, Jeff Gilbert, Peyman Milanfar

This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains.

Classification General Classification +2

Discovering Multi-Hardware Mobile Models via Architecture Search

no code implementations18 Aug 2020 Grace Chu, Okan Arikan, Gabriel Bender, Weijun Wang, Achille Brighton, Pieter-Jan Kindermans, Hanxiao Liu, Berkin Akin, Suyog Gupta, Andrew Howard

Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored.

Neural Architecture Search

Can weight sharing outperform random architecture search? An investigation with TuNAS

1 code implementation CVPR 2020 Gabriel Bender, Hanxiao Liu, Bo Chen, Grace Chu, Shuyang Cheng, Pieter-Jan Kindermans, Quoc Le

Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models.

Image Classification Neural Architecture Search

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

3 code implementations CVPR 2021 Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen

By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators.

Neural Architecture Search Object Detection

BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models

1 code implementation ECCV 2020 Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, Quoc Le

Without extra retraining or post-processing steps, we are able to train a single set of shared weights on ImageNet and use these weights to obtain child models whose sizes range from 200 to 1000 MFLOPs.

Neural Architecture Search

Neural Predictor for Neural Architecture Search

1 code implementation ECCV 2020 Wei Wen, Hanxiao Liu, Hai Li, Yiran Chen, Gabriel Bender, Pieter-Jan Kindermans

First we train N random architectures to generate N (architecture, validation accuracy) pairs and use them to train a regression model that predicts accuracy based on the architecture.

Neural Architecture Search

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

7 code implementations NeurIPS 2019 Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam

We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks.

General Classification Image Classification +1

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