STL-10 dataset

swMATH ID: 39164
Software Authors: Adam Coates, Honglak Lee, Andrew Y. Ng
Description: The STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled examples is provided to learn image models prior to supervised training. The primary challenge is to make use of the unlabeled data (which comes from a similar but different distribution from the labeled data) to build a useful prior. We also expect that the higher resolution of this dataset (96x96) will make it a challenging benchmark for developing more scalable unsupervised learning methods. Reference: Adam Coates, Honglak Lee, Andrew Y. Ng An Analysis of Single Layer Networks in Unsupervised Feature Learning
Homepage: https://cs.stanford.edu/~acoates/stl10/
Related Software: CIFAR; AlexNet; ImageNet; darch; AdaGrad; Adam; MNIST; U-Net; GitHub; GloVe; Inception-v4; FaceNet; DeepFace; DGM; Evolino; word2vec; LIBSVM; Entropy-SGD; Saga; Outex
Cited in: 23 Publications

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