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 48 articles )

Showing results 1 to 20 of 48.
Sorted by year (citations)

1 2 3 next

  1. Calder, Jeff: The game theoretic (p)-Laplacian and semi-supervised learning with few labels (2019)
  2. Diaz-Chito, Katerine; Martínez del Rincón, Jesús; Rusiñol, Marçal; Hernández-Sabaté, Aura: Feature extraction by using dual-generalized discriminative common vectors (2019)
  3. Kaiyang Zhou, Tao Xiang: Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch (2019) arXiv
  4. Sil C. van de Leemput; Jonas Teuwen; Bram van Ginneken; Rashindra Manniesing: MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks (2019) not zbMATH
  5. Taig, Efrat; Ben-Shahar, Ohad: Gradient surfing: a new deterministic approach for low-dimensional global optimization (2019)
  6. Wu, Ying Nian; Gao, Ruiqi; Han, Tian; Zhu, Song-Chun: A tale of three probabilistic families: discriminative, descriptive, and generative models (2019)
  7. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  8. Dominik Marek Loroch; Franz-Josef Pfreundt; Norbert Wehn; Janis Keuper: Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuant (2018) arXiv
  9. Lin, Xing; Rivenson, Yair; Yardimci, Nezih T.; Veli, Muhammed; Luo, Yi; Jarrahi, Mona; Ozcan, Aydogan: All-optical machine learning using diffractive deep neural networks (2018)
  10. Li, Qianxiao; Chen, Long; Tai, Cheng; E, Weinan: Maximum principle based algorithms for deep learning (2018)
  11. Pan, Yuangang; Han, Bo; Tsang, Ivor W.: Stagewise learning for noisy (k)-ary preferences (2018)
  12. Raviv, Dolev; Hazan, Tamir; Osadchy, Margarita: Hinge-minimax learner for the ensemble of hyperplanes (2018)
  13. Wang, Yuzhu; Arns, Christoph H.; Rahman, Sheik S.; Arns, Ji-Youn: Porous structure reconstruction using convolutional neural networks (2018)
  14. Wortman Vaughan, Jennifer: Making better use of the crowd: how crowdsourcing can advance machine learning research (2018)
  15. Xie, Hao; Du, Yunyan; Yu, Huapeng; Chang, Yongxin; Xu, Zhiyong; Tang, Yuanyan: Open set face recognition with deep transfer learning and extreme value statistics (2018)
  16. Yu, Felix X.; Bhaskara, Aditya; Kumar, Sanjiv; Gong, Yunchao; Chang, Shih-Fu: On binary embedding using circulant matrices (2018)
  17. Zheng, Wenjie; Bellet, Aurélien; Gallinari, Patrick: A distributed Frank-Wolfe framework for learning low-rank matrices with the trace norm (2018)
  18. Cardoso, Douglas O.; Gama, João; França, Felipe M. G.: Weightless neural networks for open set recognition (2017)
  19. Dominik Marek Loroch; Norbert Wehn; Franz-Josef Pfreundt; Janis Keuper: TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization (2017) arXiv
  20. Fehri, Amin; Velasco-Forero, Santiago; Meyer, Fernand: Prior-based hierarchical segmentation highlighting structures of interest (2017)

1 2 3 next


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