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 92 articles )

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  1. Carlsson, Gunnar; Gabrielsson, Rickard Brüel: Topological approaches to deep learning (2020)
  2. Chen, Ruidian; He, Jingsong: Two-stage training method of retinanet for bird’s nest detection (2020)
  3. Christoph Heindl, Lukas Brunner, Sebastian Zambal, Josef Scharinger: BlendTorch: A Real-Time, Adaptive Domain Randomization Library (2020) arXiv
  4. 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
  5. Frazier-Logue, Noah; Hanson, Stephen José: The stochastic delta rule: faster and more accurate deep learning through adaptive weight noise (2020)
  6. Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)
  7. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  8. Gokhale, Angelina; Pande, Mandaar B.; Pramod, Dhanya: Implementation of a quantum transfer learning approach to image splicing detection (2020)
  9. Gühring, Ingo; Kutyniok, Gitta; Petersen, Philipp: Error bounds for approximations with deep ReLU neural networks in (W^s , p) norms (2020)
  10. Kossaifi, Jean; Lipton, Zachary C.; Kolbeinsson, Arinbjorn; Khanna, Aran; Furlanello, Tommaso; Anandkumar, Anima: Tensor regression networks (2020)
  11. Lermé, Nicolas; Le Hégarat-Mascle, Sylvie; Malgouyres, François; Lachaize, Marie: Multilayer joint segmentation using MRF and graph cuts (2020)
  12. Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste: Bayesian active learning for production, a systematic study and a reusable library (2020) arXiv
  13. P.E. Hadjidoukas, A. Bartezzaghi, F. Scheidegger, R. Istrate, C.Bekas, A.C.I. Malossi: torcpy: Supporting task parallelism in Python (2020) not zbMATH
  14. Shen, Yexin; Cao, Jiuwen; Wang, Jianzhong; Yang, Zhixin: Urban acoustic classification based on deep feature transfer learning (2020)
  15. Sodhani, Shagun; Chandar, Sarath; Bengio, Yoshua: Toward training recurrent neural networks for lifelong learning (2020)
  16. Teng, Hao; Lu, Huijuan; Ye, Minchao; Yan, Ke; Gao, Zhigang; Jin, Qun: Applying of adaptive threshold non-maximum suppression to pneumonia detection (2020)
  17. Valaitis, Vytautas; Marcinkevicius, Virginijus; Jurevicius, Rokas: Learning aerial image similarity using triplet networks (2020)
  18. Wang, Yi; Zhang, Hao; Chae, Kum Ju; Choi, Younhee; Jin, Gong Yong; Ko, Seok-Bum: Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography (2020)
  19. Xiao, Heng; Wu, Jin-Long; Laizet, Sylvain; Duan, Lian: Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations (2020)
  20. Axel Barroso-Laguna, Edgar Riba, Daniel Ponsa, Krystian Mikolajczyk: Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters (2019) arXiv

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