  Learning from examples with noisy labels has attracted increasing attention
recently. But, this paper will show that the commonly used CIFAR-based datasets
and the accuracy evaluation metric used in the literature are both
inappropriate in this context. An alternative valid evaluation metric and new
datasets are proposed in this paper to promote proper research and evaluation
in this area. Then, friends and foes are identified from existing methods as
technical components that are either beneficial or detrimental to deep learning
from noisy labeled examples, respectively, and this paper improves and combines
technical components from the friends category, including self-supervised
learning, new warmup strategy, instance filtering and label correction. The
resulting F&F method significantly outperforms existing methods on the proposed
nCIFAR datasets and the real-world Clothing1M dataset.
