  The existence of noisy data is prevalent in both the training and testing
phases of machine learning systems, which inevitably leads to the degradation
of model performance. There have been plenty of works concentrated on learning
with in-distribution (IND) noisy labels in the last decade, i.e., some training
samples are assigned incorrect labels that do not correspond to their true
classes. Nonetheless, in real application scenarios, it is necessary to
consider the influence of out-of-distribution (OOD) samples, i.e., samples that
do not belong to any known classes, which has not been sufficiently explored
yet. To remedy this, we study a new problem setup, namely Learning with
Open-world Noisy Data (LOND). The goal of LOND is to simultaneously learn a
classifier and an OOD detector from datasets with mixed IND and OOD noise. In
this paper, we propose a new graph-based framework, namely Noisy Graph Cleaning
(NGC), which collects clean samples by leveraging geometric structure of data
and model predictive confidence. Without any additional training effort, NGC
can detect and reject the OOD samples based on the learned class prototypes
directly in testing phase. We conduct experiments on multiple benchmarks with
different types of noise and the results demonstrate the superior performance
of our method against state of the arts.
