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.

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References in zbMATH (referenced in 94 articles )

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  1. 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)
  2. Carlsson, Gunnar; Gabrielsson, Rickard Brüel: Topological approaches to deep learning (2020)
  3. Chen, Ruidian; He, Jingsong: Two-stage training method of retinanet for bird’s nest detection (2020)
  4. Christoph Heindl, Lukas Brunner, Sebastian Zambal, Josef Scharinger: BlendTorch: A Real-Time, Adaptive Domain Randomization Library (2020) arXiv
  5. 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
  6. Frazier-Logue, Noah; Hanson, Stephen José: The stochastic delta rule: faster and more accurate deep learning through adaptive weight noise (2020)
  7. Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)
  8. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  9. Gokhale, Angelina; Pande, Mandaar B.; Pramod, Dhanya: Implementation of a quantum transfer learning approach to image splicing detection (2020)
  10. Gühring, Ingo; Kutyniok, Gitta; Petersen, Philipp: Error bounds for approximations with deep ReLU neural networks in (W^s , p) norms (2020)
  11. Jin, Yuan; Carman, Mark; Zhu, Ye; Xiang, Yong: A technical survey on statistical modelling and design methods for crowdsourcing quality control (2020)
  12. Kossaifi, Jean; Lipton, Zachary C.; Kolbeinsson, Arinbjorn; Khanna, Aran; Furlanello, Tommaso; Anandkumar, Anima: Tensor regression networks (2020)
  13. Lermé, Nicolas; Le Hégarat-Mascle, Sylvie; Malgouyres, François; Lachaize, Marie: Multilayer joint segmentation using MRF and graph cuts (2020)
  14. Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste: Bayesian active learning for production, a systematic study and a reusable library (2020) arXiv
  15. P.E. Hadjidoukas, A. Bartezzaghi, F. Scheidegger, R. Istrate, C.Bekas, A.C.I. Malossi: torcpy: Supporting task parallelism in Python (2020) not zbMATH
  16. Shen, Yexin; Cao, Jiuwen; Wang, Jianzhong; Yang, Zhixin: Urban acoustic classification based on deep feature transfer learning (2020)
  17. Sodhani, Shagun; Chandar, Sarath; Bengio, Yoshua: Toward training recurrent neural networks for lifelong learning (2020)
  18. Teng, Hao; Lu, Huijuan; Ye, Minchao; Yan, Ke; Gao, Zhigang; Jin, Qun: Applying of adaptive threshold non-maximum suppression to pneumonia detection (2020)
  19. Valaitis, Vytautas; Marcinkevicius, Virginijus; Jurevicius, Rokas: Learning aerial image similarity using triplet networks (2020)
  20. 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)

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