ImageNet

ImageNet is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a ”synonym set” or ”synset”. There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy.


References in zbMATH (referenced in 521 articles )

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  1. Chen, Qipin; Hao, Wenrui; He, Juncai: A weight initialization based on the linear product structure for neural networks (2022)
  2. Adcock, Ben; Dexter, Nick: The gap between theory and practice in function approximation with deep neural networks (2021)
  3. Ao, Wenqi; Li, Wenbin; Qian, Jianliang: A data and knowledge driven approach for SPECT using convolutional neural networks and iterative algorithms (2021)
  4. Ayensa-Jiménez, Jacobo; Doweidar, Mohamed H.; Sanz-Herrera, Jose A.; Doblaré, Manuel: Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (2021)
  5. Babu, G. Jogesh; Banks, David; Cho, Hyunsoon; Han, David; Sang, Hailin; Wang, Shouyi: A statistician teaches deep learning (2021)
  6. Cauchois, Maxime; Gupta, Suyash; Duchi, John C.: Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction (2021)
  7. Celledoni, Elena; Ehrhardt, Matthias J.; Etmann, Christian; Owren, Brynjulf; Schönlieb, Carola-Bibiane; Sherry, Ferdia: Equivariant neural networks for inverse problems (2021)
  8. Chen, Tianbo; Sun, Ying; Li, Ta-Hsin: A semi-parametric estimation method for the quantile spectrum with an application to earthquake classification using convolutional neural network (2021)
  9. Chi, Heng; Zhang, Yuyu; Tang, Tsz Ling Elaine; Mirabella, Lucia; Dalloro, Livio; Song, Le; Paulino, Glaucio H.: Universal machine learning for topology optimization (2021)
  10. Christopher P. Bridge, Chris Gorman, Steven Pieper, Sean W. Doyle, Jochen K. Lennerz, Jayashree Kalpathy-Cramer, David A. Clunie, Andriy Y. Fedorov, Markus D. Herrmann: Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology (2021) arXiv
  11. De Loera, Jesús A.; Haddock, Jamie; Ma, Anna; Needell, Deanna: Data-driven algorithm selection and tuning in optimization and signal processing (2021)
  12. Effland, Alexander; Kobler, Erich; Pock, Thomas; Rajković, Marko; Rumpf, Martin: Image morphing in deep feature spaces: theory and applications (2021)
  13. Fan, Jianqing; Ma, Cong; Zhong, Yiqiao: A selective overview of deep learning (2021)
  14. Fresca, Stefania; Dede’, Luca; Manzoni, Andrea: A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (2021)
  15. Gambella, Claudio; Ghaddar, Bissan; Naoum-Sawaya, Joe: Optimization problems for machine learning: a survey (2021)
  16. Gao, Yu; Zhang, Kai: Machine learning based data retrieval for inverse scattering problems with incomplete data (2021)
  17. Ghods, Alireza; Cook, Diane J.: A survey of deep network techniques all classifiers can adopt (2021)
  18. Gordon, Andrew S. (ed.); Miller, Rob (ed.); Morgenstern, Leora (ed.); Turán, György (ed.): Preface (2021)
  19. Gossmann, Alexej; Pezeshk, Aria; Wang, Yu-Ping; Sahiner, Berkman: Test data reuse for the evaluation of continuously evolving classification algorithms using the area under the receiver operating characteristic curve (2021)
  20. Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, Maria Gabrani: HistoCartography: A Toolkit for Graph Analytics in Digital Pathology (2021) arXiv

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Further publications can be found at: http://image-net.org/about-publication