  Corruptions due to data perturbations and label noise are prevalent in the
datasets from unreliable sources, which poses significant threats to model
training. Despite existing efforts in developing robust models, current
learning methods commonly overlook the possible co-existence of both
corruptions, limiting the effectiveness and practicability of the model. In
this paper, we develop an Effective and Robust Adversarial Training (ERAT)
framework to simultaneously handle two types of corruption (i.e., data and
label) without prior knowledge of their specifics. We propose a hybrid
adversarial training surrounding multiple potential adversarial perturbations,
alongside a semi-supervised learning based on class-rebalancing sample
selection to enhance the resilience of the model for dual corruption. On the
one hand, in the proposed adversarial training, the perturbation generation
module learns multiple surrogate malicious data perturbations by taking a DNN
model as the victim, while the model is trained to maintain semantic
consistency between the original data and the hybrid perturbed data. It is
expected to enable the model to cope with unpredictable perturbations in
real-world data corruption. On the other hand, a class-rebalancing data
selection strategy is designed to fairly differentiate clean labels from noisy
labels. Semi-supervised learning is performed accordingly by discarding noisy
labels. Extensive experiments demonstrate the superiority of the proposed ERAT
framework.
