Learning from Noisy Large-Scale Datasets with Minimal Supervision

@article{Veit2017LearningFN,
  title={Learning from Noisy Large-Scale Datasets with Minimal Supervision},
  author={Andreas Veit and Neil Gordon Alldrin and Gal Chechik and Ivan Krasin and Abhinav Kumar Gupta and Serge J. Belongie},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={6575-6583}
}
We present an approach to effectively use millions of images with noisy annotations in conjunction with a small subset of cleanly-annotated images to learn powerful image representations. One common approach to combine clean and noisy data is to first pre-train a network using the large noisy dataset and then fine-tune with the clean dataset. We show this approach does not fully leverage the information contained in the clean set. Thus, we demonstrate how to use the clean annotations to reduce… 
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