SqueezeNAS: Fast neural architecture search for faster semantic segmentation | Paper Citation Record

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This paper cites Imagenet: A large-scale hierarchical image database,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Imagenet: A large-scale hierarchical image database,

Reference 1

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Observation 3aa37f3e-98b9-413b-985a-78f0bfdf9cdf · outbound

This paper cites Learning multiple layers of features from tiny images,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Learning multiple layers of features from tiny images,

Reference 2

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This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 3

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Observation 2995c6e5-154f-473e-8d47-7dc79d20b0c0 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 4

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This paper cites The unreasonable effectiveness of deep fea- tures as a perceptual metric,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation The unreasonable effectiveness of deep fea- tures as a perceptual metric,

Reference 5

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Observation 6556bbb3-669d-4b8f-86bc-2229d5ae736e · outbound

This paper cites Reg- ularized evolution for image classiﬁer architecture search,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Reg- ularized evolution for image classiﬁer architecture search,

Reference 6

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Observation 84acf76a-7d90-4a39-83c4-f0354376bcf7 · outbound

This paper cites Neural architecture search with reinforcement learning,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Neural architecture search with reinforcement learning,

Reference 7

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Observation c6f689fc-65ba-4054-8d9a-faf6d4c9976e · outbound

This paper cites DARTS: Differentiable architecture search,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation DARTS: Differentiable architecture search,

Reference 8

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Observation c7cf4cca-363e-4de4-8ca1-e44534889e74 · outbound

This paper cites Fbnet: Hardware-aware efﬁcient convnet design via differ- entiable neural architecture search,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Fbnet: Hardware-aware efﬁcient convnet design via differ- entiable neural architecture search,

Reference 9

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Observation bf99b2f6-d938-475d-81a6-a6e31931a4f9 · outbound

This paper cites EmBench: Quantifying performance variations of deep neural networks across modern com- modity devices,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation EmBench: Quantifying performance variations of deep neural networks across modern com- modity devices,

Reference 10

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Observation 39acbe75-acfc-4d74-8898-7365b593200b · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation The cityscapes dataset for semantic urban scene understanding,

Reference 11

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Observation 2611fcbf-31d3-4d85-a28a-5d83612c44e8 · outbound

This paper cites Jetson AGX Xavier developer kit,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Jetson AGX Xavier developer kit,

Reference 12

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Observation c9496d9d-861a-4594-92bb-e58989be2856 · outbound

This paper cites Scene parsing through ade20k dataset,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Scene parsing through ade20k dataset,

Reference 13

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Observation bfb16e08-74b8-43b1-b8b9-e6e4258bafaf · outbound

This paper cites Indoor segmentation and support inference from rgbd images,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Indoor segmentation and support inference from rgbd images,

Reference 14

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Observation fde3edc6-1c09-4790-a37f-2bac4a7f49d0 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation The pascal visual object classes (voc) challenge,

Reference 15

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Observation b35a6070-636e-48f2-b366-5ea62d5588e9 · outbound

This paper cites Im- agenet classiﬁcation with deep convolutional neural networks,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Im- agenet classiﬁcation with deep convolutional neural networks,

Reference 16

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Observation 32e348ee-c460-4217-8489-3961ce75954c · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Fully convolutional networks for semantic segmentation,

Reference 17

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Observation 441ee8a8-2847-4892-9827-6d5e2e6223fa · outbound

This paper cites DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Reference 18

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Observation 18d790db-c0dc-434c-a1d1-bbe864c1f4e7 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 19

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Observation 2f9f549a-ab0c-4426-be6b-138d9c8f8baf · outbound

This paper cites Encoder-decoder with atrous separable con- volution for semantic image segmentation,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Encoder-decoder with atrous separable con- volution for semantic image segmentation,

Reference 20

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Observation 3ec7ec67-34d4-4a46-b215-ceeb4cc85e6e · outbound

This paper cites Visualizing and understand- ing convolutional networks,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Visualizing and understand- ing convolutional networks,

Reference 21

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Observation 96e09021-38f9-400b-9a64-e0ebf59c9417 · outbound

This paper cites Deep residual learning for image recognition,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Deep residual learning for image recognition,

Reference 22

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Observation fbe58285-7a27-4a32-b5fc-31c6c8ae18e6 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 23

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This paper cites Shufﬂenet: An extremely efﬁcient convolutional neural network for mobile devices,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Shufﬂenet: An extremely efﬁcient convolutional neural network for mobile devices,

Reference 24

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Observation c311cc4d-f490-4e93-937f-7830c86bd44c · outbound

This paper cites Search- ing for efﬁcient multi-scale architectures for dense im- age prediction,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Search- ing for efﬁcient multi-scale architectures for dense im- age prediction,

Reference 25

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Observation 93bef7d9-eaff-4f10-b955-2c8ef0c6c7fc · outbound

This paper cites Nas-fpn: Learn- ing scalable feature pyramid architecture for object detection,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Nas-fpn: Learn- ing scalable feature pyramid architecture for object detection,

Reference 26

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Observation 64485492-57c9-44f6-b141-9c570d7ff1fc · outbound

This paper cites Focal loss for dense object detection,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Focal loss for dense object detection,

Reference 27

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Observation a5a6e376-cd91-4904-bb54-4ff178f6ce0b · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region pro- posal networks,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Faster r-cnn: Towards real-time object detection with region pro- posal networks,

Reference 28

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Observation 998b5d2e-787b-4066-b2a0-4db82c243462 · outbound

This paper cites SNAS: stochastic neural architecture search,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation SNAS: stochastic neural architecture search,

Reference 29

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Observation 8a753022-2b9c-4822-ad0b-16b2f4e2274a · outbound

This paper cites ProxylessNAS: Direct neural architecture search on target task and hardware,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation ProxylessNAS: Direct neural architecture search on target task and hardware,

Reference 30

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Observation c6c3f27a-1892-4615-8a37-6d446fe74230 · outbound

This paper cites Meta Architecture Search.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Meta Architecture Search

Reference 31

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Observation 009e9eb0-a973-4af5-9379-a19e0f6d6d17 · outbound

This paper cites Efﬁcient neural architecture search via parameters sharing,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Efﬁcient neural architecture search via parameters sharing,

Reference 32

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Observation 7fd874e4-840d-49d0-9f31-1684517889ff · outbound

This paper cites Progressive neural architecture search,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Progressive neural architecture search,

Reference 33

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Observation b4b42d68-a111-4293-aa06-8c1c162ce9dd · outbound

This paper cites Learn- ing transferable architectures for scalable image recog- nition,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Learn- ing transferable architectures for scalable image recog- nition,

Reference 34

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This paper cites Mnasnet: Platform-aware neural architecture search for mobile,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Mnasnet: Platform-aware neural architecture search for mobile,

Reference 35

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Observation 0babaddc-428d-4d6e-bbb9-31e9872d4dcc · outbound

This paper cites Searching for MobileNetV3.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Searching for MobileNetV3

Reference 36

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This paper cites Ef- ﬁcient architecture search by network transformation,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Ef- ﬁcient architecture search by network transformation,

Reference 37

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Observation 6c398b8e-0b50-4f9e-9233-e3e55bd2666a · outbound

This paper cites Auto-deeplab: Hierar- chical neural architecture search for semantic image segmentation,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Auto-deeplab: Hierar- chical neural architecture search for semantic image segmentation,

Reference 38

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Observation f64246dd-1632-42d0-9c13-e410fa01d9f9 · outbound

This paper cites Categorical reparame- terization with gumbel-softmax,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Categorical reparame- terization with gumbel-softmax,

Reference 39

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Observation ccff0d53-286a-499e-ae58-d98b9671b4a2 · outbound

This paper cites MobileNetV2: Inverted residuals and linear bottlenecks,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation MobileNetV2: Inverted residuals and linear bottlenecks,

Reference 40

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Observation 6222f758-fc8d-4b54-887b-df93e05d11b9 · outbound

This paper cites C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation

Reference 41

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Observation 8fafae1a-3b8e-4f0c-90f6-56bb7943b903 · outbound

This paper cites Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation

Reference 42

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This paper cites Microsoft COCO: Common objects in context,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Microsoft COCO: Common objects in context,

Reference 43

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Observation 4e298345-686f-487d-8d32-c261228bbd3f · outbound

This paper cites Megdet: A large mini-batch object detector,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Megdet: A large mini-batch object detector,

Reference 44

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Observation c1c29c37-d392-4a5a-91fa-82a21717536c · outbound

This paper cites Batch normalization: Ac- celerating deep network training by reducing internal covariate shift,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Batch normalization: Ac- celerating deep network training by reducing internal covariate shift,

Reference 45

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Observation 886a003a-fe94-4f15-ab63-38c5846dfaf2 · outbound

This paper cites Squeeze-and-excitation networks,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Squeeze-and-excitation networks,

Reference 46

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Observation 6401428e-7aab-4f2e-9dcc-5861ada07c78 · outbound

This paper cites Searching for Activation Functions.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Searching for Activation Functions

Reference 47

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Observation 08a55c84-20f4-4631-b413-099ef8f56d89 · outbound

This paper cites Pyramid scene parsing network,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Pyramid scene parsing network,

Reference 48

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Observation 3ffe813c-34be-4949-b533-07df896cf117 · outbound

This paper cites Rooﬂine: An insightful visual performance model for ﬂoating- point programs and multicore architectures,.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Rooﬂine: An insightful visual performance model for ﬂoating- point programs and multicore architectures,

Reference 49

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Observation 6e94c8e7-c8f8-4981-8ae4-81168e74a5df · outbound

This paper cites Available: http://arxiv.org/abs/1710.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Available: http://arxiv.org/abs/1710

Reference 2017

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Observation d49d27bd-45f9-45f6-92ac-9f01b44f4827 · outbound

This paper cites Available: http://arxiv.org/abs/1809.

SqueezeNAS: Fast neural architecture search for faster semantic segmentation Available: http://arxiv.org/abs/1809

Reference 2018

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