{
  "citation": "\n@inproceedings{coates2011stl10,\n  title={{An Analysis of Single Layer Networks in Unsupervised Feature Learning}},\n  author={Coates, Adam and Ng, Andrew and Lee, Honglak},\n  booktitle={AISTATS},\n  year={2011},\n  note = {\\url{https://cs.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf}},\n}\n",
  "description": "The STL-10 dataset is an image recognition dataset for developing unsupervised\nfeature learning, deep learning, self-taught learning algorithms. It is inspired\nby the CIFAR-10 dataset but with some modifications. In particular, each class\nhas fewer labeled training examples than in CIFAR-10, but a very large set of \nunlabeled examples is provided to learn image models prior to supervised\ntraining. The primary challenge is to make use of the unlabeled data (which\ncomes from a similar but different distribution from the labeled data) to build\na useful prior. All images were acquired from labeled examples on ImageNet.\n",
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  "location": {
    "urls": [
      "http://ai.stanford.edu/~acoates/stl10/"
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