  In many classification problems, collecting massive clean-annotated data is
not easy, and thus a lot of researches have been done to handle data with noisy
labels. Most recent state-of-art solutions for noisy label problems are built
on the small-loss strategy which exploits the memorization effect. While it is
a powerful tool, the memorization effect has several drawbacks. The
performances are sensitive to the choice of a training epoch required for
utilizing the memorization effect. In addition, when the labels are heavily
contaminated or imbalanced, the memorization effect may not occur in which case
the methods based on the small-loss strategy fail to identify clean labeled
data. We introduce a new method called INN(Integration with the Nearest
Neighborhoods) to refine clean labeled data from training data with noisy
labels. The proposed method is based on a new discovery that a prediction
pattern at neighbor regions of clean labeled data is consistently different
from that of noisy labeled data regardless of training epochs. The INN method
requires more computation but is much stable and powerful than the small-loss
strategy. By carrying out various experiments, we demonstrate that the INN
method resolves the shortcomings in the memorization effect successfully and
thus is helpful to construct more accurate deep prediction models with training
data with noisy labels.
