Understanding Deep Learning on Controlled Noisy Labels

Posted by Lu Jiang, Senior Research Scientist and Weilong Yang, Senior Staff Software Engineer, Google Research The success of deep neural networks depends on access to high-quality labeled training data, as the presence of label errors (label noise) in training data can greatly reduce the accuracy of models on clean test data. Unfortunately, large training datasets almost always contain examples with inaccurate or incorrect labels. This leads to a paradox: on one hand, large datasets are necess...

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