  Deep neural networks (DNNs) have advanced many machine learning tasks, but
their performance is often harmed by noisy labels in real-world data.
Addressing this, we introduce CoLafier, a novel approach that uses Local
Intrinsic Dimensionality (LID) for learning with noisy labels. CoLafier
consists of two subnets: LID-dis and LID-gen. LID-dis is a specialized
classifier. Trained with our uniquely crafted scheme, LID-dis consumes both a
sample's features and its label to predict the label - which allows it to
produce an enhanced internal representation. We observe that LID scores
computed from this representation effectively distinguish between correct and
incorrect labels across various noise scenarios. In contrast to LID-dis,
LID-gen, functioning as a regular classifier, operates solely on the sample's
features. During training, CoLafier utilizes two augmented views per instance
to feed both subnets. CoLafier considers the LID scores from the two views as
produced by LID-dis to assign weights in an adapted loss function for both
subnets. Concurrently, LID-gen, serving as classifier, suggests pseudo-labels.
LID-dis then processes these pseudo-labels along with two views to derive LID
scores. Finally, these LID scores along with the differences in predictions
from the two subnets guide the label update decisions. This dual-view and
dual-subnet approach enhances the overall reliability of the framework. Upon
completion of the training, we deploy the LID-gen subnet of CoLafier as the
final classification model. CoLafier demonstrates improved prediction accuracy,
surpassing existing methods, particularly under severe label noise. For more
details, see the code at https://github.com/zdy93/CoLafier.
