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).
Keywords for this software
References in zbMATH (referenced in 100 articles )
Showing results 1 to 20 of 100.
Sorted by year (- Avelin, Benny; Nyström, Kaj: Neural ODEs as the deep limit of ResNets with constant weights (2021)
- Cauchois, Maxime; Gupta, Suyash; Duchi, John C.: Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction (2021)
- Cheng, Yichen; Wang, Xinlei; Xia, Yusen: Supervised (t)-distributed stochastic neighbor embedding for data visualization and classification (2021)
- Cristofari, Andrea; Rinaldi, Francesco: A derivative-free method for structured optimization problems (2021)
- Czaja, Wojciech; Dong, Dong; Jabin, Pierre-Emmanuel; Ndjakou Njeunje, Franck Olivier: Transport model for feature extraction (2021)
- 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)
- 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
- Hao, Jie; Zhu, William: Architecture self-attention mechanism: nonlinear optimization for neural architecture search (2021)
- Kafka, Dominic; Wilke, Daniel N.: Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (2021)
- 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
- Northcutt, Curtis G.; Jiang, Lu; Chuang, Isaac L.: Confident learning: estimating uncertainty in dataset labels (2021)
- Ramezani-Kebrya, Ali; Faghri, Fartash; Markov, Ilya; Aksenov, Vitalii; Alistarh, Dan; Roy, Daniel M.: NUQSGD: provably communication-efficient data-parallel SGD via nonuniform quantization (2021)
- Yang, Hongfei; Ding, Xiaofeng; Chan, Raymond; Hu, Hui; Peng, Yaxin; Zeng, Tieyong: A new initialization method based on normed statistical spaces in deep networks (2021)
- Aryal, Sunil; Ting, Kai Ming; Washio, Takashi; Haffari, Gholamreza: A comparative study of data-dependent approaches without learning in measuring similarities of data objects (2020)
- 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)
- Carlsson, Gunnar; Gabrielsson, Rickard Brüel: Topological approaches to deep learning (2020)
- Cui, Zhenghang; Charoenphakdee, Nontawat; Sato, Issei; Sugiyama, Masashi: Classification from triplet comparison data (2020)
- Duan, Shiyu; Yu, Shujian; Chen, Yunmei; Principe, Jose C.: On kernel method-based connectionist models and supervised deep learning without backpropagation (2020)
- Frazier-Logue, Noah; Hanson, Stephen José: The stochastic delta rule: faster and more accurate deep learning through adaptive weight noise (2020)
- Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)