Learning with Noisy Labels: Interconnection of Two
  Expectation-Maximizations
Heewon Kim Hyun Sung Chang Kiho Cho Jaeyun Lee Bohyung Han
Labor-intensive labeling becomes a bottleneck in developing computer vision
algorithms based on deep learning. For this reason, dealing with imperfect
labels has increasingly gained attention and has become an active field of
study. We address learning with noisy labels (LNL) problem, which is formalized
as a task of finding a structured manifold in the midst of noisy data. In this
framework, we provide a proper objective function and an optimization algorithm
based on two expectation-maximization (EM) cycles. The separate networks
associated with the two EM cycles collaborate to optimize the objective
function, where one model is for distinguishing clean labels from corrupted
ones while the other is for refurbishing the corrupted labels. This approach
results in a non-collapsing LNL-flywheel model in the end. Experiments show
that our algorithm achieves state-of-the-art performance in multiple standard
benchmarks with substantial margins under various types of label noise.