FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
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Yuandong Tian | Kurt Keutzer | Peter Vajda | Yangqing Jia | Yiming Wu | Xiaoliang Dai | Fei Sun | Peizhao Zhang | Bichen Wu | Yanghan Wang | Yangqing Jia | Bichen Wu | K. Keutzer | Yuandong Tian | Péter Vajda | Xiaoliang Dai | Peizhao Zhang | Yanghan Wang | Fei Sun | Yiming Wu
[1] Forrest N. Iandola,et al. SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[2] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[3] Liang Lin,et al. SNAS: Stochastic Neural Architecture Search , 2018, ICLR.
[4] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[5] Yee Whye Teh,et al. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.
[6] Bo Chen,et al. NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications , 2018, ECCV.
[7] Song Han,et al. AMC: AutoML for Model Compression and Acceleration on Mobile Devices , 2018, ECCV.
[8] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Quoc V. Le,et al. Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.
[11] Bo Chen,et al. MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[13] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[14] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[15] Yuandong Tian,et al. Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search , 2018, ArXiv.
[16] Luciano Lavagno,et al. Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs , 2018, FPGA.
[17] Ludovic Denoyer,et al. Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[18] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[19] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[20] Kurt Keutzer,et al. SqueezeNext: Hardware-Aware Neural Network Design , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[21] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[22] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[23] Kurt Keutzer,et al. SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[24] Xiangyu Zhang,et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.
[25] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[27] Kilian Q. Weinberger,et al. CondenseNet: An Efficient DenseNet Using Learned Group Convolutions , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[28] Li Fei-Fei,et al. Progressive Neural Architecture Search , 2017, ECCV.
[29] Kurt Keutzer,et al. SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[30] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[31] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Kurt Keutzer,et al. Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.