  Incorrectly labeled examples, or label noise, is common in real-world
computer vision datasets. While the impact of label noise on learning in deep
neural networks has been studied in prior work, these studies have exclusively
focused on homogeneous label noise, i.e., the degree of label noise is the same
across all categories. However, in the real-world, label noise is often
heterogeneous, with some categories being affected to a greater extent than
others. Here, we address this gap in the literature. We hypothesized that
heterogeneous label noise would only affect the classes that had label noise
unless there was transfer from those classes to the classes without label
noise. To test this hypothesis, we designed a series of computer vision studies
using MNIST, CIFAR-10, CIFAR-100, and MS-COCO where we imposed heterogeneous
label noise during the training of multi-class, multi-task, and multi-label
systems. Our results provide evidence in support of our hypothesis: label noise
only affects the class affected by it unless there is transfer.
