CIFAR

The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. The CIFAR-100 dataset: This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a ”fine” label (the class to which it belongs) and a ”coarse” label (the superclass to which it belongs).


References in zbMATH (referenced in 116 articles )

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  1. Avelin, Benny; Nyström, Kaj: Neural ODEs as the deep limit of ResNets with constant weights (2021)
  2. Castera, Camille; Bolte, Jérôme; Févotte, Cédric; Pauwels, Edouard: An inertial Newton algorithm for deep learning (2021)
  3. Cauchois, Maxime; Gupta, Suyash; Duchi, John C.: Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction (2021)
  4. Cheng, Yichen; Wang, Xinlei; Xia, Yusen: Supervised (t)-distributed stochastic neighbor embedding for data visualization and classification (2021)
  5. Cristofari, Andrea; Rinaldi, Francesco: A derivative-free method for structured optimization problems (2021)
  6. Czaja, Wojciech; Dong, Dong; Jabin, Pierre-Emmanuel; Ndjakou Njeunje, Franck Olivier: Transport model for feature extraction (2021)
  7. Galvan, Giulio; Lapucci, Matteo; Lin, Chih-Jen; Sciandrone, Marco: A two-level decomposition framework exploiting first and second order information for SVM training problems (2021)
  8. Gao, Qingyi; Wang, Xiao: Theoretical investigation of generalization bounds for adversarial learning of deep neural networks (2021)
  9. Ghods, Alireza; Cook, Diane J.: A survey of deep network techniques all classifiers can adopt (2021)
  10. Haiping Lu, Xianyuan Liu, Robert Turner, Peizhen Bai, Raivo E Koot, Shuo Zhou, Mustafa Chasmai, Lawrence Schobs: PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python (2021) arXiv
  11. Hao, Jie; Zhu, William: Architecture self-attention mechanism: nonlinear optimization for neural architecture search (2021)
  12. Harris, Ethan; Mihai, Daniela; Hare, Jonathon: How convolutional neural network architecture biases learned opponency and color tuning (2021)
  13. Huang, Junhao; Sun, Weize; Huang, Lei: Joint structure and parameter optimization of multiobjective sparse neural network (2021)
  14. Imaizumi, Masaaki: Analysis on mechanism of deep learning: perspective of generalization error (2021)
  15. Jones, Ilenna Simone; Kording, Konrad Paul: Might a single neuron solve interesting machine learning problems through successive computations on its dendritic tree? (2021)
  16. Kafka, Dominic; Wilke, Daniel N.: Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (2021)
  17. Kao, Yu-Wei; Chen, Hung-Hsuan: Associated learning: decomposing end-to-end backpropagation based on autoencoders and target propagation (2021)
  18. Kobayashi, Masaki: Stability conditions of bicomplex-valued Hopfield neural networks (2021)
  19. Kobayashi, Masaki: Noise robust projection rule for Klein Hopfield neural networks (2021)
  20. Mingxiang Chen, Zhanguo Chang, Haonan Lu, Bitao Yang, Zhuang Li, Liufang Guo, Zhecheng Wang: AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation (2021) arXiv

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