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 128 articles )

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  1. Haghighat, Ehsan; Juanes, Ruben: SciANN: a keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (2021)
  2. Hao, Jie; Zhu, William: Architecture self-attention mechanism: nonlinear optimization for neural architecture search (2021)
  3. Hua, Michelle; Gao, Mingqi; Zhong, Zichun: SCN: dilated silhouette convolutional network for video action recognition (2021)
  4. Kwanyoung Park, Hyunseok Oh, Youngki Lee: VECA : A Toolkit for Building Virtual Environments to Train and Test Human-like Agents (2021) arXiv
  5. Li, Zhihan; Fan, Yuwei; Ying, Lexing: Multilevel fine-tuning: closing generalization gaps in approximation of solution maps under a limited budget for training data (2021)
  6. Urbaniak, Ilona; Wolter, Marcin: Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network (2021)
  7. Zejiang Shen, Ruochen Zhang, Melissa Dell, Benjamin Charles Germain Lee, Jacob Carlson, Weining Li: LayoutParser Toolkit Document Image (2021) arXiv
  8. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  9. Bullock, Joseph; Luccioni, Alexandra; Pham, Katherine Hoffman; Lam, Cynthia Sin Nga; Luengo-Oroz, Miguel: Mapping the landscape of artificial intelligence applications against COVID-19 (2020)
  10. Calder, Jeff; Slepčev, Dejan: Properly-weighted graph Laplacian for semi-supervised learning (2020)
  11. Carlsson, Gunnar; Gabrielsson, Rickard Brüel: Topological approaches to deep learning (2020)
  12. Chen, Ruidian; He, Jingsong: Two-stage training method of retinanet for bird’s nest detection (2020)
  13. Chen, Ruizhi; Li, Ling: Analyzing and accelerating the bottlenecks of training deep SNNs with backpropagation (2020)
  14. Christoph Heindl, Lukas Brunner, Sebastian Zambal, Josef Scharinger: BlendTorch: A Real-Time, Adaptive Domain Randomization Library (2020) arXiv
  15. Daryanavard, Sama; Porr, Bernd: Closed-loop deep learning: generating forward models with backpropagation (2020)
  16. Fernando Pérez-García, Rachel Sparks, Sebastien Ourselin: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) arXiv
  17. Frazier-Logue, Noah; Hanson, Stephen José: The stochastic delta rule: faster and more accurate deep learning through adaptive weight noise (2020)
  18. Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)
  19. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  20. Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)

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