Baker B, Gupta O, Naik N, Raskar R (2017) “Designing Neural Network Architectures using Reinforcement Learning”. In: 5th international conference on learning representations.

Bender G, Kindermans PJ, Zoph, Vasudevan B, V. and Le, Q (2018) Understanding and simplifying one-shot architecture search. In international conference on machine learning, July, pp. 550–559.

Brock A, Lim T, Ritchie JM. and Weston N (2017) SMASH: One-shot model architecture search through hypernetworks. arXiv preprint arXiv:1708.05344

Cai H, Chen T, Zhang W. et al (2017) Efficient architecture search by network transformation. arXiv preprint arXiv:1707.04873

Cai H, Zhu L, Han S (2019) Proxylessnas: Direct neural architecture search on target task and hardware (ICLR 2019). arXiv preprint arXiv:1812.00332.

Cai K, Miao X, Wang WH. Pang Y Liu and J. Song (2020) A modified YOLOv3 model for fish detection based on mobilenetv1 as backbone. aquacultural engineering 91: 102117.

Chen X. and Hsieh, Ch (2020) Stabilizing differentiable architecture search via perturbation-based regularization. In ICML

Chen LC, Collins M, Zhu Y. et al. H. Adam and J. Shlens (2018) Searching for efficient multi-scale architectures for dense image prediction. In Advances in neural information processing systems, pages 8713–8724,.

Chu X, Wang X, Zhang B, Lu S, Wei, X. and Yan J (2021) DARTS-: Robustly stepping out of performance collapse without indicators. In international conference on learning representations

Courbariaux M, Hubara I, Soudry D. et al (2016) Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830

Deng J, Dong W, Socher R. et al (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255

Denton EL, Zaremba W, Bruna J. and LeCun YFergus R (2014) Exploiting linear structure within convolutional networks for efcient evaluation. In: advances in neural information processing systems, pp 1269–1277.

Dong X. and Yang Y (2019a) Searching for a robust neural architecture in four gpu hours. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 1761–1770

Elsken T, Metzen JH, Hutter F (2019) Neural Architecture Search: A Survey. J Mach Learn Res 20:1–21

Google Scholar 

Everingham M, Van Gool L, Williams CKI et al (2010) The Pascal Visual Object Classes (VOC) Challenge. Int J Comput Vision 88(2):303–338

Article  Google Scholar 

Ha D, Dai A, Le Q V (2017) Hypernetworks. In International conference on learning representations (ICLR)

Han S, Pool J, Tran J et al (2015) Learning both weights and connections for efficient neural network. Adv Neural Inf Process Syst 28:1135–1143

Google Scholar 

Han S, Mao H, Dally WJ (2015) Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149

Hassibi B, Stork DG (1992) Second order derivatives for network pruning: Optimal Brain Surgeon, In: advances in neural information processing systems., pp. 164–171.

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 770–778.

He Y, LinJ, Liu Z, Wang H, Li LJ, Han, S (2018) AMC: AutoML for model compression and acceleration on mobile devices. In Proceedings of the European conference on computer vision (ECCV), pp. 784–800.

Hinton G, Vinyals O, Dean J (2014) Distilling the knowledge in a neural network, In: NIPS Workshop

Hou L, Kwok JT (2018) Loss-aware weight quantization of deep networks. arXiv preprint arXiv:1802.08635,.

Howard AG, Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications, CoRR vol. abs/1704.04861

Hu H, Peng R, Tai YW, Tang CK (2016) Network trimming: a data-driven neuron pruning approach towards efficient deep architectures, arXiv preprint arXiv:1607.03250.

Hu J, Shen L, and Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition.: 7132–7141.

Huang G, Liu Z, Maaten L vd., Weinberger K.Q (2017) Densely Connected Convolutional Networks, In: IEEE conference on computer vision and pattern recognition (CVPR) pp. 4700–4708.

Hundt A, Jain V, Hager G. D (2019) sharpDARTS: Faster and more accurate differentiable architecture search. arXiv:1903.09900

Jaderberg M, Vedaldi A, Zisserman A (2014) Speeding up convolutional neural networks with low rank expansions. arXiv preprint arXiv:1405.3866

Jin H, Song, Q, Hu X. (2018) Auto-keras: Efficient neural architecture search with network morphism,.

Krizhevsky A., Hinton G (2009) Learning multiple layers of features from tiny images, in: Tech Report,

LeCun Y, Denker JS, Solla SA (1989) Optimal brain damage. In: advances in neural information processing systems, pp. 598–605.

Li, L., and Talwalkar, A. Random search and reproducibility for neural architecture search. arXiv preprint arXiv:1902.07638, 2019.

Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2017) Pruning filters for efficient convnets, In: international conference on learning representations (ICLR) arXiv preprint arXiv:1608.08710.

LiY, Lin S, Liu J, Ye Q, Wang M, Chao,F., Yang, F., Ma, J., Tian, Q. and Ji, R (2021). Towards Compact CNNs via Collaborative Compression, In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 6438–6447.

Lin T, Maire M, Belongie S, Hays J, Perona, P, Ramanan, D, Dollar, Pi. and Zitnick, C L. (2014) Microsoft coco: Common objects in context. In ECCV

Liu W, Anguelov D, Erhan D. et al (2016) SSD: Single Shot MultiBox Detector; European conference on computer vision. Springer, Cham, 21–37.

Liu Z, Li J, Shen Z, Huang Yan, G, S. and Zhang, C (2017) Learning efficient convolutional networks through network slimming, In: Proceedings of the IEEE international conference on computer vision, , pp. 2755–2763.

Liu H, Simonyan K, Yang Y (2019a) DARTS: Differentiable architecture search. In international conference on learning representations (ICLR), arXiv:1806.09055.

Liu Z, MuH, Zhang, X, Guo Z, Yang X, Cheng KT. and Sun J 2019b Metapruning: Meta learning for automatic neural network channel pruning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 3296–3305.

Liu J, Wang X (2020) Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model. Plant Methods 16(1):1–16

Article  Google Scholar 

Ma N, Zhang X, Zheng HT, Sun J (2018) ShuffleNet V2: practical guidelines for efficient cnn architecture design. arXiv preprint arXiv:1807.11164v1

Miller GF., Todd PM., Hegde SU (1989) Designing neural networks using genetic algorithms. ICGA. 89

Mirzadeh SI, Farajtabar M, Li A et al (2020) Improved knowledge distillation via teacher assistant, Proceedings of the AAAI Conference on. Artif Intell 34(04):5191–5198

Google Scholar 

Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY B (2011) Reading digits in natural images with unsupervised feature learning, in: NIPS workshop on deep learning and unsupervised feature learning

Pham H., Guan MY, Zoph B, Le QV. and Dean J (2018) Faster discovery of neural architectures by searching for paths in a large model. International conference on learning representations

Phan AH, Sobolev K, Sozykin K. et al (2020) Stable low-rank tensor decomposition for compression of convolutional neural network[C]//European Conference on Computer Vision. Springer, Cham,: 522–539.

Real E, Aggarwal A, Huang Y. et al (2018) Regularized evolution for image classifier architecture search., arXiv:1802.01548.

Real E, Aggarwal A, Huang Y and Le, QV (2019) Regularized evolution for image classifier architecture search. In AAAI conference on artificial intelligence (AAAI), pages 4780–4789

Redmon J. and Farhadi A (2018) YOLOv3: An incremental improvement. arXiv preprint arXiv: 1804.02767

Redmon, J., Divvala, S., Girshick, R. et al. You Only Look Once: Unified, Real-Time Object Detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 779–788.

Ren S, He K, Girshick R. et al Faster R-CNN: Towards real-time object detection with region proposal networks, arXiv preprint arXiv:1506.01497.

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C. Chen (2018) Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. arXiv preprint arXiv:1801.04381

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556,.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A (2015) Going deeper with convolutions, In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 1–9.

Szegedy C, Vanhoucke Ioffe V, Shlens S, Wojna J, Z (2016) Rethinking the inception architecture for computer vision, In: IEEE conference on computer vision and pattern recognition (CVPR), , pp. 2818–2826.

Tung F, Mori G (2019) Similarity-preserving knowledge distillation. In Proceedings of the IEEE international conference on computer vision 1365–1374.

Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The caltech-ucsd birds-200–2011 dataset

Wang W, Zhu L (2020) Structured feature sparsity training for convolutional neural network compression. J vis Commun Image Represent 71:102867

Article  Google Scholar 

Wang W, Zhu L, Guo B (2019) Reliable identification of redundant kernels for convolutional neural network compression. J vis Commun Image Represent 63:102582

Article  Google Scholar 

Wang H, Lohit S, Jones M. et al (2020) Multi-head knowledge distillation for model compression. arXiv preprint arXiv:2012.02911

Wang RJ, Li X. and Ling CX (2018) Pelee: A real-time object detection system on mobile devices. Advances in neural information processing systems 31.

Wen W, Wu C, Wang Y, Chen Y, Li H (2016) Learning structured sparsity in deep neural networks. In: advances in neural information processing systems., pp. 2074–2082.

White, C., Neiswanger, W. and Savani, Y. Bananas:Bayesian optimization with neural architectures for neural architecture search. In AAAI, 2021

Xie L, Yuille A. Genetic CNN. (2017) In IEEE international conference on computer vision (ICCV) , arXiv:1703.01513.

Xie S, Zheng H, Liu C. and Lin L (2019) SNAS: stochastic neural architecture search. In international conference on learning representations

Yin Z, Yiu V, Hu X et al (2021) End-to-end face parsing via interlinked convolutional neural networks. Cogn Neurodyn 15:169–179

Article  PubMed  Google Scholar 

Zhang X, Zhou X, Lin M, Sun J (2017) Shufflenet: An extremely efficient convolutional neural network for mobile devices. arXiv preprint arXiv:1707.01083

Zhang C, Ren M. and Urtasun, R (2019) Graph hypernetworks for neural architecture search. In international conference on learning representations (ICLR)

Zhou A, Yao A, Wang K. et al (2018) Explicit loss-error-aware quantization for low-bit deep neural networks, In: Proceedings of the IEEE conference on computer vision and pattern recognition. 9426–9435.

Zoph B, Le QV (2017) Neural architecture search with reinforcement learning. In international conference on learning representations (ICLR)

Zoph B, Vasudevan V, J. Shlens and Le, Q. V. (2018)Learning transferable architectures for scalable image recognition. In conference on computer vision and pattern recognition