暂无分享,去创建一个
Yuandong Tian | Joseph E. Gonzalez | Zhen Wei | Peter Vajda | Zijian He | Xiaoliang Dai | Peizhao Zhang | Alvin Wan | Bichen Wu | Matthew Yu | Kan Chen | Bichen Wu | Joseph Gonzalez | Alvin Wan | Yuandong Tian | Zijian He | Péter Vajda | Zhen Wei | Xiaoliang Dai | Peizhao Zhang | Kan Chen | Matthew Yu
[1] Song Han,et al. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware , 2018, ICLR.
[2] Ion Stoica,et al. Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules , 2019, ICML.
[3] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[4] Fei Yang,et al. Efficient Segmentation: Learning Downsampling Near Semantic Boundaries , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[5] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[6] David D. Cox,et al. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures , 2013, ICML.
[7] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[8] Ion Stoica,et al. Tune: A Research Platform for Distributed Model Selection and Training , 2018, ArXiv.
[9] Quoc V. Le,et al. Searching for MobileNetV3 , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[10] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[13] Yuandong Tian,et al. FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Quoc V. Le,et al. Large-Scale Evolution of Image Classifiers , 2017, ICML.
[15] 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.
[16] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[17] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[18] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[20] Hanxiao Liu,et al. Neural Predictor for Neural Architecture Search , 2019, ECCV.
[21] 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).
[22] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Kilian Q. Weinberger,et al. Deep Networks with Stochastic Depth , 2016, ECCV.
[24] Yuandong Tian,et al. FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Gustavo Carneiro,et al. A Bayesian Data Augmentation Approach for Learning Deep Models , 2017, NIPS.
[26] Xiangyu Zhang,et al. Single Path One-Shot Neural Architecture Search with Uniform Sampling , 2019, ECCV.
[27] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[28] Niraj K. Jha,et al. ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Bichen Wu,et al. SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation , 2020, ECCV.
[30] Chongruo Wu,et al. ResNeSt: Split-Attention Networks , 2020, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[31] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[32] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, ArXiv.
[33] Bo Chen,et al. MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Yingwei Li,et al. AtomNAS: Fine-Grained End-to-End Neural Architecture Search , 2020, ICLR.
[35] Xiangyu Zhang,et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.
[36] Chuang Gan,et al. Once for All: Train One Network and Specialize it for Efficient Deployment , 2019, ICLR.
[37] Liang Lin,et al. SNAS: Stochastic Neural Architecture Search , 2018, ICLR.
[38] Bo Chen,et al. NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications , 2018, ECCV.
[39] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[40] Derek Hoiem,et al. Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Song Han,et al. Trained Ternary Quantization , 2016, ICLR.
[42] Lalit M. Patnaik,et al. Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..
[43] Kaiming He,et al. Designing Network Design Spaces , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[45] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[46] Niraj K. Jha,et al. NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm , 2017, IEEE Transactions on Computers.
[47] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[49] Quoc V. Le,et al. AutoAugment: Learning Augmentation Strategies From Data , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Kaiming He,et al. Exploring the Limits of Weakly Supervised Pretraining , 2018, ECCV.