  Deep neural networks (DNNs) are being widely applied for various real-world
applications across domains due to their high performance (e.g., high accuracy
on image classification). Nevertheless, a well-trained DNN after deployment
could oftentimes raise errors during practical use in the operational
environment due to the mismatching between distributions of the training
dataset and the potential unknown noise factors in the operational environment,
e.g., weather, blur, noise etc. Hence, it poses a rather important problem for
the DNNs' real-world applications: how to repair the deployed DNNs for
correcting the failure samples (i.e., incorrect prediction) under the deployed
operational environment while not harming their capability of handling normal
or clean data. The number of failure samples we can collect in practice, caused
by the noise factors in the operational environment, is often limited.
Therefore, It is rather challenging how to repair more similar failures based
on the limited failure samples we can collect.
  In this paper, we propose a style-guided data augmentation for repairing DNN
in the operational environment. We propose a style transfer method to learn and
introduce the unknown failure patterns within the failure data into the
training data via data augmentation. Moreover, we further propose the
clustering-based failure data generation for much more effective style-guided
data augmentation. We conduct a large-scale evaluation with fifteen degradation
factors that may happen in the real world and compare with four
state-of-the-art data augmentation methods and two DNN repairing methods,
demonstrating that our method can significantly enhance the deployed DNNs on
the corrupted data in the operational environment, and with even better
accuracy on clean datasets.
