  Data lies at the core of modern deep learning. The impressive performance of
supervised learning is built upon a base of massive accurately labeled data.
However, in some real-world applications, accurate labeling might not be
viable; instead, multiple noisy labels (instead of one accurate label) are
provided by several annotators for each data sample. Learning a classifier on
such a noisy training dataset is a challenging task. Previous approaches
usually assume that all data samples share the same set of parameters related
to annotator errors, while we demonstrate that label error learning should be
both annotator and data sample dependent. Motivated by this observation, we
propose a novel learning algorithm. The proposed method displays superiority
compared with several state-of-the-art baseline methods on MNIST, CIFAR-100,
and ImageNet-100. Our code is available at:
https://github.com/zhengqigao/Learning-from-Multiple-Annotator-Noisy-Labels.
