Network Transplanting

Keywords

Bottleneck, Computer science, Task (project management), Transplanting, Modular design

References

• Very Deep Convolutional Networks for Large-Scale Image RecognitionKaren Simonyan, Andrew Zisserman — 2014
• Distilling the Knowledge in a Neural NetworkGeoffrey E. Hinton, Oriol Vinyals, Jay B. Dean — 2015
• Fully convolutional networks for semantic segmentationJonathan Long, Evan Shelhamer, Trevor Darrell — 2014
• A convolutional neural network cascade for face detectionHaoxiang Li, Zhe Lin, Xiaohui Shen — 2015
• Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body PartsXianjie Chen, Roozbeh Mottaghi, Xiaobai Liu — 2014
• Gradient-based learning applied to document recognitionYann LeCun, Léon Bottou, Yoshua Bengio — 1998
• Semantic contours from inverse detectorsBharath Hariharan, Pablo Arbeláez, Lubomir Bourdev — 2011
• ImageNet classification with deep convolutional neural networksAlex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton — 2012
• Deep Residual Learning for Image RecognitionKaiming He, Xiangyu Zhang, Shaoqing Ren — 2015
• Deep Neural Decision ForestsPeter Kontschieder, Madalina Fiterau, Antonio Criminisi — 2015
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsXi Chen, Yan Duan, Rein Houthooft — 2016
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts LayerNoam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz — 2017
• PathNet: Evolution Channels Gradient Descent in Super Neural NetworksChrisantha Fernando, Dylan Banarse, Charles Blundell — 2017
• Opening the Black Box of Deep Neural Networks via InformationRavid Shwartz-Ziv, Naftali Tishby — 2017
• Learning Transferable Architectures for Scalable Image RecognitionBarret Zoph, Vijay Vasudevan, Jonathon Shlens — 2017
• Practical Network Blocks Design with Q-LearningZhao Zhong, Junjie Yan, Cheng‐Lin Liu — 2017
• beta-VAE: Learning Basic Visual Concepts with a Constrained Variational FrameworkIrina Higgins, Löıc Matthey, Arka Pal — 2016
• Interpretable Convolutional Neural NetworksQuanshi Zhang, Ying Wu, Song‐Chun Zhu — 2017
• Interpretable R-CNNTianfu Wu, Xilai Li, Xi Song — 2017
• Beyond Sparsity: Tree Regularization of Deep Models for InterpretabilityMike Wu, Michael Hughes, Sonali Parbhoo — 2017

Cited by

• Interpreting CNNs via Decision TreesQuanshi Zhang, Yang Yu, Haotian Ma — 2018
• Mining a Deep And-OR Object Semantics from Web Images via Cost-Sensitive Question-Answer-Based Active AnnotationsQuanshi Zhang, Ying Wu, Hao Zhang — 2017
• CX-ToM: Counterfactual explanations with theory-of-mind for enhancing human trust in image recognition modelsArjun Akula, Keze Wang, Changsong Liu — 2021
• Modularity in Deep Learning: A SurveySun, Haozhe, Guyon, Isabelle — 2023

Related papers

• Root response of Merion Kentucky bluegrass sods to various nitrogen applications near the time of transplanting.J. H. Dunn, R. E. Engel — 1970
• Effect of different transplanting dates on growth and yield of tomato crop under naturally ventilated polyhouseD.D. Pardhe, S.R. Bhakar, M.K. Kothari — 2022
• Effects of Different Transplanting Methods on Growth of Flue-cured Tobacco in FieldJia Rui-la — 2013
• Techno-economic performance of imported kukje self-propelled rice transplanterMuhammad Abdul Munnaf, MM Hossain, Farida Yasmin Ruma — 2014
• Determining the Best Planting Density in Mechanized Transplanting of Rice Compared to Manual Transplanting in Conditions of High Seedling AgeEhsan Eyshi Rezaei, Morteza Sam Daliri, H R Mobasser — 2023
• Comparative Experiment of Different Transplanting Methods in Mechanized Rice transplantingHU You-fa — 2010
• Comparison of parachute, line and traditional rice transplanting methods at farmer's field in rice growing areaTahir Hussain Awan, Izhar Ali, Muhammad Ehsan Safdar — 2008
• Study on Transplanting Technology of Tissue Culture Seedlings of Dracaena godseffianaTian Hai — 2013