A Comprehensive Survey of Neural Architecture Search
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Zhihui Li | Xiaojiang Chen | Xiaojun Chang | Pengzhen Ren | Xin Wang | Yun Xiao | Po-yao Huang | Xiaojun Chang | Xin Wang | Xiaojiang Chen | Yun Xiao | Zhihui Li | Pengzhen Ren | Po-yao Huang
[1] Song Han,et al. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware , 2018, ICLR.
[2] Chuang Gan,et al. Once for All: Train One Network and Specialize it for Efficient Deployment , 2019, ICLR.
[3] Michael S. Ryoo,et al. AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures , 2019, ICLR.
[4] Katarzyna Musial,et al. NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size , 2020, ArXiv.
[5] Thomas Brox,et al. AutoDispNet: Improving Disparity Estimation With AutoML , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[6] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[7] Mehrtash Harandi,et al. Hierarchical Neural Architecture Search for Deep Stereo Matching , 2020, NeurIPS.
[8] Rongrong Ji,et al. Multinomial Distribution Learning for Effective Neural Architecture Search , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] Xiangyu Zhang,et al. DetNAS: Neural Architecture Search on Object Detection , 2019, ArXiv.
[10] Jungong Han,et al. Deep Attentive Video Summarization With Distribution Consistency Learning , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[11] Trung Le,et al. MGAN: Training Generative Adversarial Nets with Multiple Generators , 2018, ICLR.
[12] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[13] Patrice Marcotte,et al. An overview of bilevel optimization , 2007, Ann. Oper. Res..
[14] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Shaofeng Cai,et al. Understanding Architectures Learnt by Cell-based Neural Architecture Search , 2020, ICLR.
[16] Hao Chen,et al. Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Gaofeng Meng,et al. DATA: Differentiable ArchiTecture Approximation , 2019, NeurIPS.
[18] Shuicheng Yan,et al. Dual Path Networks , 2017, NIPS.
[19] Theodore Lim,et al. SMASH: One-Shot Model Architecture Search through HyperNetworks , 2017, ICLR.
[20] 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.
[21] Ian R. Lane,et al. Speeding up Hyper-parameter Optimization by Extrapolation of Learning Curves Using Previous Builds , 2017, ECML/PKDD.
[22] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[23] Yi Yang,et al. One-Shot Neural Architecture Search via Self-Evaluated Template Network , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[24] Tianqi Chen,et al. Net2Net: Accelerating Learning via Knowledge Transfer , 2015, ICLR.
[25] Li Fei-Fei,et al. Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Shao Tiefeng,et al. Cocoon Image Segmentation Method Based on Fully Convolutional Networks , 2020 .
[27] 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).
[28] Tao Mei,et al. Customizable Architecture Search for Semantic Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Xiaojun Chang,et al. Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Li Fei-Fei,et al. Progressive Neural Architecture Search , 2017, ECCV.
[31] Kevin Leyton-Brown,et al. Sequential Model-Based Optimization for General Algorithm Configuration , 2011, LION.
[32] Wei Wu,et al. Practical Block-Wise Neural Network Architecture Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[33] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[34] Marc'Aurelio Ranzato,et al. Large Scale Distributed Deep Networks , 2012, NIPS.
[35] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Xiaopeng Zhang,et al. PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search , 2020, ICLR.
[37] Tie-Yan Liu,et al. Neural Architecture Optimization , 2018, NeurIPS.
[38] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[39] Junwei Han,et al. From Discriminant to Complete: Reinforcement Searching-Agent Learning for Weakly Supervised Object Detection , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[40] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[41] Donald R. Jones,et al. A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..
[42] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[43] Kilian Q. Weinberger,et al. Deep Networks with Stochastic Depth , 2016, ECCV.
[44] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[45] Shih-Fu Chang,et al. ConvNet Architecture Search for Spatiotemporal Feature Learning , 2017, ArXiv.
[46] Concetto Spampinato,et al. MASK-RL: Multiagent Video Object Segmentation Framework Through Reinforcement Learning , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[47] Kirthevasan Kandasamy,et al. Neural Architecture Search with Bayesian Optimisation and Optimal Transport , 2018, NeurIPS.
[48] Xiaofang Wang,et al. Learnable Embedding Space for Efficient Neural Architecture Compression , 2019, ICLR.
[49] Ramesh Raskar,et al. Accelerating Neural Architecture Search using Performance Prediction , 2017, ICLR.
[50] Wei Wang,et al. Improving MMD-GAN Training with Repulsive Loss Function , 2018, ICLR.
[51] Frank Hutter,et al. Simple And Efficient Architecture Search for Convolutional Neural Networks , 2017, ICLR.
[52] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[54] Terry L. Friesz,et al. Hierarchical optimization: An introduction , 1992, Ann. Oper. Res..
[55] 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).
[56] Quoc V. Le,et al. Large-Scale Evolution of Image Classifiers , 2017, ICML.
[57] Oriol Vinyals,et al. Hierarchical Representations for Efficient Architecture Search , 2017, ICLR.
[58] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] 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.
[60] Masanori Suganuma,et al. A genetic programming approach to designing convolutional neural network architectures , 2017, GECCO.
[61] Shiyu Chang,et al. AutoGAN: Neural Architecture Search for Generative Adversarial Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[62] George Papandreou,et al. Searching for Efficient Multi-Scale Architectures for Dense Image Prediction , 2018, NeurIPS.
[63] Ryan P. Adams,et al. SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers , 2019, NeurIPS.
[64] Yunyang Xiong,et al. Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help? , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[65] Huiqi Li,et al. Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity Maximization , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[66] Quoc V. Le,et al. AutoHAS: Differentiable Hyper-parameter and Architecture Search , 2020, ArXiv.
[67] Yong Yu,et al. Efficient Architecture Search by Network Transformation , 2017, AAAI.
[68] Xinggang Wang,et al. Fast Neural Network Adaptation via Parameter Remapping and Architecture Search , 2020, ICLR.
[69] Stefan Carlsson,et al. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[70] Quoc V. Le,et al. Understanding and Simplifying One-Shot Architecture Search , 2018, ICML.
[71] Quoc V. Le,et al. AutoAugment: Learning Augmentation Strategies From Data , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[72] Jun Xie,et al. Neural Machine Translation With GRU-Gated Attention Model , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[73] Michael S. Ryoo,et al. Evolving Space-Time Neural Architectures for Videos , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[74] Zhangyang Wang,et al. AutoSpeech: Neural Architecture Search for Speaker Recognition , 2020, INTERSPEECH.
[75] Alan L. Yuille,et al. Genetic CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[76] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[77] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[78] Lihi Zelnik-Manor,et al. XNAS: Neural Architecture Search with Expert Advice , 2019, NeurIPS.
[79] Jakob Verbeek,et al. Convolutional Neural Fabrics , 2016, NIPS.
[80] Pouya Bashivan,et al. Teacher Guided Architecture Search , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[81] Graham W. Taylor,et al. Improved Regularization of Convolutional Neural Networks with Cutout , 2017, ArXiv.
[82] Wei Pan,et al. BayesNAS: A Bayesian Approach for Neural Architecture Search , 2019, ICML.
[83] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[84] Frank Hutter,et al. Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves , 2015, IJCAI.
[85] Lorenzo Torresani,et al. MaskConnect: Connectivity Learning by Gradient Descent , 2018, ECCV.
[86] Song Han,et al. Path-Level Network Transformation for Efficient Architecture Search , 2018, ICML.
[87] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[88] Frank Hutter,et al. NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search , 2020, ICLR.
[89] Quoc V. Le,et al. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[90] Chuan Zhou,et al. One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[91] Dahua Lin,et al. PolyNet: A Pursuit of Structural Diversity in Very Deep Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[92] Wei Wu,et al. Improving One-Shot NAS by Suppressing the Posterior Fading , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[93] Bo Chen,et al. MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[94] Quoc V. Le,et al. Multi-task Sequence to Sequence Learning , 2015, ICLR.
[95] Junjie Yan,et al. Peephole: Predicting Network Performance Before Training , 2017, ArXiv.
[96] Yuandong Tian,et al. FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function , 2020, ArXiv.
[97] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[98] Thomas Brox,et al. Understanding and Robustifying Differentiable Architecture Search , 2020, ICLR.
[99] Aaron Klein,et al. Learning Curve Prediction with Bayesian Neural Networks , 2016, ICLR.
[100] Martin Wistuba,et al. Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations , 2018, ECML/PKDD.
[101] Quoc V. Le,et al. Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.
[102] Ngai-Man Cheung,et al. Dist-GAN: An Improved GAN Using Distance Constraints , 2018, ECCV.
[103] Rui Xu,et al. When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[104] Qian Zhang,et al. Densely Connected Search Space for More Flexible Neural Architecture Search , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[105] Yi Yang,et al. Searching for a Robust Neural Architecture in Four GPU Hours , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[106] Yi Yang,et al. Network Pruning via Transformable Architecture Search , 2019, NeurIPS.
[107] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[108] Raquel Urtasun,et al. Graph HyperNetworks for Neural Architecture Search , 2018, ICLR.
[109] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[110] Ameet Talwalkar,et al. Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization , 2016, ICLR.
[111] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[112] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[113] Geoffrey J. Gordon,et al. DeepArchitect: Automatically Designing and Training Deep Architectures , 2017, ArXiv.
[114] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[115] Frank Hutter,et al. Neural Architecture Search: A Survey , 2018, J. Mach. Learn. Res..
[116] Martin Jaggi,et al. Evaluating the Search Phase of Neural Architecture Search , 2019, ICLR.
[117] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[118] Ramakanth Pasunuru,et al. Continual and Multi-Task Architecture Search , 2019, ACL.
[119] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[120] Jiashi Feng,et al. Partial Order Pruning: For Best Speed/Accuracy Trade-Off in Neural Architecture Search , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[121] Gregory Shakhnarovich,et al. FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.
[122] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[123] Aaron Klein,et al. NAS-Bench-101: Towards Reproducible Neural Architecture Search , 2019, ICML.
[124] Yao Zhou,et al. Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[125] Xiangyu Zhang,et al. Single Path One-Shot Neural Architecture Search with Uniform Sampling , 2019, ECCV.
[126] Ameet Talwalkar,et al. Random Search and Reproducibility for Neural Architecture Search , 2019, UAI.
[127] Qi Tian,et al. Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[128] Masakazu Iwamura,et al. Deep Pyramidal Residual Networks with Separated Stochastic Depth , 2016, ArXiv.
[129] Jingbo Zhu,et al. Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition , 2019, EMNLP.
[130] Tieniu Tan,et al. Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection , 2019, NeurIPS.
[131] Bo Zhang,et al. FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search , 2019, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[132] Ramesh Raskar,et al. Designing Neural Network Architectures using Reinforcement Learning , 2016, ICLR.
[133] Lorenzo Torresani,et al. Connectivity Learning in Multi-Branch Networks , 2017, ArXiv.
[134] Frank Hutter,et al. Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution , 2018, ICLR.
[135] Nicholas Rhinehart,et al. N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning , 2017, ICLR.
[136] Fuzhen Zhuang,et al. Deep Subdomain Adaptation Network for Image Classification , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[137] Alok Aggarwal,et al. Regularized Evolution for Image Classifier Architecture Search , 2018, AAAI.
[138] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[139] Jiaya Jia,et al. Fast and Practical Neural Architecture Search , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[140] Deniz Yuret,et al. Transfer Learning for Low-Resource Neural Machine Translation , 2016, EMNLP.
[141] Min Sun,et al. DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures , 2018, ECCV.
[142] Yingwei Li,et al. AtomNAS: Fine-Grained End-to-End Neural Architecture Search , 2020, ICLR.