Abstract
Noisy labels are unavoidable yet troublesome in the ecosystem of deep
learning because models can easily overfit them. There are many types of label
noise, such as symmetric, asymmetric and instance-dependent noise (IDN), with
IDN being the only type that depends on image information. Such dependence on
image information makes IDN a critical type of label noise to study, given that
labelling mistakes are caused in large part by insufficient or ambiguous
information about the visual classes present in images. Aiming to provide an
effective technique to address IDN, we present a new graphical modelling
approach called InstanceGM, that combines discriminative and generative models.
The main contributions of InstanceGM are: i) the use of the continuous
Bernoulli distribution to train the generative model, offering significant
training advantages, and ii) the exploration of a state-of-the-art noisy-label
discriminative classifier to generate clean labels from instance-dependent
noisy-label samples. InstanceGM is competitive with current noisy-label
learning approaches, particularly in IDN benchmarks using synthetic and
real-world datasets, where our method shows better accuracy than the
competitors in most experiments.