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 84 articles )

Showing results 1 to 20 of 84.
Sorted by year (citations)

1 2 3 4 5 next

  1. Hao, Jie; Zhu, William: Architecture self-attention mechanism: nonlinear optimization for neural architecture search (2021)
  2. Borisyak, Maxim; Ryzhikov, Artem; Ustyuzhanin, Andrey; Derkach, Denis; Ratnikov, Fedor; Mineeva, Olga: ((1 + \varepsilon))-class classification: an anomaly detection method for highly imbalanced or incomplete data sets (2020)
  3. Carlsson, Gunnar; Gabrielsson, Rickard Brüel: Topological approaches to deep learning (2020)
  4. Cui, Zhenghang; Charoenphakdee, Nontawat; Sato, Issei; Sugiyama, Masashi: Classification from triplet comparison data (2020)
  5. Duan, Shiyu; Yu, Shujian; Chen, Yunmei; Principe, Jose C.: On kernel method-based connectionist models and supervised deep learning without backpropagation (2020)
  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. Georgiev, Dobromir; Gurov, Todor: Distributed deep learning on heterogeneous computing resources using gossip communication (2020)
  9. Gu, Xue; Meng, Ziyao; Liang, Yanchun; Xu, Dong; Huang, Han; Han, Xiaosong; et al.: ESAE: evolutionary strategy-based architecture evolution (2020)
  10. Janisch, Jaromír; Pevný, Tomáš; Lisý, Viliam: Classification with costly features as a sequential decision-making problem (2020)
  11. Kobayashi, Masaki: Split quaternion-valued twin-multistate Hopfield neural networks (2020)
  12. Kobayashi, Masaki: Bicomplex projection rule for complex-valued Hopfield neural networks (2020)
  13. Kobayashi, Masaki: Hyperbolic-valued Hopfield neural networks in synchronous mode (2020)
  14. Liu, Fanghui; Huang, Xiaolin; Gong, Chen; Yang, Jie; Li, Li: Learning data-adaptive non-parametric kernels (2020)
  15. Nakada, Ryumei; Imaizumi, Masaaki: Adaptive approximation and generalization of deep neural network with intrinsic dimensionality (2020)
  16. Ogal’tsov, A. V.; Tyurin, A. I.: A heuristic adaptive fast gradient method in stochastic optimization problems (2020)
  17. Panahi, Ashkan; Chehreghani, Morteza Haghir; Dubhashi, Devdatt: Accelerated proximal incremental algorithm schemes for non-strongly convex functions (2020)
  18. Park, Seonho; Jung, Seung Hyun; Pardalos, Panos M.: Combining stochastic adaptive cubic regularization with negative curvature for nonconvex optimization (2020)
  19. Romano, Yaniv; Aberdam, Aviad; Sulam, Jeremias; Elad, Michael: Adversarial noise attacks of deep learning architectures: stability analysis via sparse-modeled signals (2020)
  20. Rousseau, François; Drumetz, Lucas; Fablet, Ronan: Residual networks as flows of diffeomorphisms (2020)

1 2 3 4 5 next