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 117 articles )

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  1. Hao, Jie; Zhu, William: Architecture self-attention mechanism: nonlinear optimization for neural architecture search (2021)
  2. Urbaniak, Ilona; Wolter, Marcin: Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network (2021)
  3. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  4. 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)
  5. Carlsson, Gunnar; Gabrielsson, Rickard Brüel: Topological approaches to deep learning (2020)
  6. Chen, Ruidian; He, Jingsong: Two-stage training method of retinanet for bird’s nest detection (2020)
  7. Chen, Ruizhi; Li, Ling: Analyzing and accelerating the bottlenecks of training deep SNNs with backpropagation (2020)
  8. Christoph Heindl, Lukas Brunner, Sebastian Zambal, Josef Scharinger: BlendTorch: A Real-Time, Adaptive Domain Randomization Library (2020) arXiv
  9. Daryanavard, Sama; Porr, Bernd: Closed-loop deep learning: generating forward models with backpropagation (2020)
  10. 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
  11. Frazier-Logue, Noah; Hanson, Stephen José: The stochastic delta rule: faster and more accurate deep learning through adaptive weight noise (2020)
  12. Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)
  13. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  14. Geng, Zhenglin; Johnson, Daniel; Fedkiw, Ronald: Coercing machine learning to output physically accurate results (2020)
  15. Gokhale, Angelina; Pande, Mandaar B.; Pramod, Dhanya: Implementation of a quantum transfer learning approach to image splicing detection (2020)
  16. Grinchuk, O. V.; Tsurkov, V. I.: Training a multimodal neural network to determine the authenticity of images (2020)
  17. Gühring, Ingo; Kutyniok, Gitta; Petersen, Philipp: Error bounds for approximations with deep ReLU neural networks in (W^s , p) norms (2020)
  18. Jin, Yuan; Carman, Mark; Zhu, Ye; Xiang, Yong: A technical survey on statistical modelling and design methods for crowdsourcing quality control (2020)
  19. Kossaifi, Jean; Lipton, Zachary C.; Kolbeinsson, Arinbjorn; Khanna, Aran; Furlanello, Tommaso; Anandkumar, Anima: Tensor regression networks (2020)
  20. Lermé, Nicolas; Le Hégarat-Mascle, Sylvie; Malgouyres, François; Lachaize, Marie: Multilayer joint segmentation using MRF and graph cuts (2020)

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