  Learning from corrupted labels is very common in real-world machine-learning
applications. Memorizing such noisy labels could affect the learning of the
model, leading to sub-optimal performances. In this work, we propose a novel
framework to learn robust machine-learning models from noisy labels. Through an
empirical study, we find that different models make relatively similar
predictions on clean examples, while the predictions on noisy examples vary
much more across different models. Motivated by this observation, we propose
\em denoising with cross-model agreement \em (DeCA) which aims to minimize the
KL-divergence between the true label distributions parameterized by two machine
learning models while maximizing the likelihood of data observation. We employ
the proposed DeCA on both the binary label scenario and the multiple label
scenario. For the binary label scenario, we select implicit feedback
recommendation as the downstream task and conduct experiments with four
state-of-the-art recommendation models on four datasets. For the multiple-label
scenario, the downstream application is image classification on two benchmark
datasets. Experimental results demonstrate that the proposed methods
significantly improve the model performance compared with normal training and
other denoising methods on both binary and multiple-label scenarios.
