Learning from Noisy Large-Scale Datasets with Minimal Supervision Learning from Noisy Large-Scale Datasets with Minimal Supervision
Paper summary _Objective:_ Predict labels using a very large dataset with noisy labels and a much smaller (3 orders of magnitude) dataset with human-verified annotations. _Dataset:_ [Open image](https://research.googleblog.com/2016/09/introducing-open-images-dataset.html) ## Architecture: Contrary to other approaches they use the clean labels, the noisy labels but also image features. They basically train 3 networks: 1. A feature extractor for the image. 2. A label Cleaning Network that predicts to learn verified labels from noisy labels + image feature. 3. An image classifier that predicts using just the image. [![screen shot 2017-04-12 at 11 10 56 am](https://cloud.githubusercontent.com/assets/17261080/24950258/c4764106-1f70-11e7-82e4-c1111ffc089e.png)](https://cloud.githubusercontent.com/assets/17261080/24950258/c4764106-1f70-11e7-82e4-c1111ffc089e.png) ## Results: Overall better performance but not breath-taking improvement: from `AP 83.832 / MAP 61.82` for a NN trained only on labels to `AP 87.67 / MAP 62.38` with their approach.

Summary by Léo Paillier 3 years ago
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