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 664 articles )

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  1. Badreddine, Samy; d’Avila Garcez, Artur; Serafini, Luciano; Spranger, Michael: Logic tensor networks (2022)
  2. Bihlo, Alex; Popovych, Roman O.: Physics-informed neural networks for the shallow-water equations on the sphere (2022)
  3. Boute, Robert N.; Gijsbrechts, Joren; van Jaarsveld, Willem; Vanvuchelen, Nathalie: Deep reinforcement learning for inventory control: a roadmap (2022)
  4. Cai, HanQin; McKenzie, Daniel; Yin, Wotao; Zhang, Zhenliang: Zeroth-order regularized optimization (ZORO): approximately sparse gradients and adaptive sampling (2022)
  5. Cai, Zhiqiang; Chen, Jingshuang; Liu, Min: Self-adaptive deep neural network: numerical approximation to functions and PDEs (2022)
  6. Caragea, Andrei; Lee, Dae Gwan; Maly, Johannes; Pfander, Götz; Voigtlaender, Felix: Quantitative approximation results for complex-valued neural networks (2022)
  7. Chen, Qipin; Hao, Wenrui; He, Juncai: A weight initialization based on the linear product structure for neural networks (2022)
  8. Dang, Wei-Dong; Lv, Dong-Mei; Li, Ru-Mei; Rui, Lin-Ge; Yang, Zhuo-Yi; Ma, Chao; Gao, Zhong-Ke: Multilayer network-based CNN model for emotion recognition (2022)
  9. Dash, Tirtharaj; Srinivasan, Ashwin; Baskar, A.: Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (2022)
  10. Daubechies, I.; DeVore, R.; Foucart, S.; Hanin, B.; Petrova, G.: Nonlinear approximation and (Deep) ReLU networks (2022)
  11. Duru, Cihat; Alemdar, Hande; Baran, Ozgur Ugras: A deep learning approach for the transonic flow field predictions around airfoils (2022)
  12. Fornasier, Massimo; Klock, Timo; Rauchensteiner, Michael: Robust and resource-efficient identification of two hidden layer neural networks (2022)
  13. Gribonval, Rémi; Kutyniok, Gitta; Nielsen, Morten; Voigtlaender, Felix: Approximation spaces of deep neural networks (2022)
  14. Guan, Zoe; Parmigiani, Giovanni; Braun, Danielle; Trippa, Lorenzo: Prediction of hereditary cancers using neural networks (2022)
  15. Gurova, Silvi-Maria; Karaivanova, Aneta: Monte Carlo method for estimating eigenvalues using error balancing (2022)
  16. Jäger, Georg; Reisinger, Daniel: Can we replicate real human behaviour using artificial neural networks? (2022)
  17. Jiang, Wei; Meng, Xianglian; Xi, Ji: Multilevel attention and multiscale feature fusion network for author classification of Chinese ink-wash paintings (2022)
  18. Knoblauch, Andreas: On the antiderivatives of (x^p/(1 - x)) with an application to optimize loss functions for classification with neural networks (2022)
  19. Lai, Jianfa; Weng, Lin-Chen; Peng, Xiaoling; Fang, Kai-Tai: Construction of symmetric orthogonal designs with deep Q-network and orthogonal complementary design (2022)
  20. Li, Haoya; Ying, Lexing: A semigroup method for high dimensional elliptic PDEs and eigenvalue problems based on neural networks (2022)

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