Enhancing Accuracy and Robustness through Adversarial Training in Class
  Incremental Continual Learning
Minchan Kwon Kangil Kim
In real life, adversarial attack to deep learning models is a fatal security
issue. However, the issue has been rarely discussed in a widely used
class-incremental continual learning (CICL). In this paper, we address problems
of applying adversarial training to CICL, which is well-known defense method
against adversarial attack. A well-known problem of CICL is class-imbalance
that biases a model to the current task by a few samples of previous tasks.
Meeting with the adversarial training, the imbalance causes another imbalance
of attack trials over tasks. Lacking clean data of a minority class by the
class-imbalance and increasing of attack trials from a majority class by the
secondary imbalance, adversarial training distorts optimal decision boundaries.
The distortion eventually decreases both accuracy and robustness than
adversarial training. To exclude the effects, we propose a straightforward but
significantly effective method, External Adversarial Training (EAT) which can
be applied to methods using experience replay. This method conduct adversarial
training to an auxiliary external model for the current task data at each time
step, and applies generated adversarial examples to train the target model. We
verify the effects on a toy problem and show significance on CICL benchmarks of
image classification. We expect that the results will be used as the first
baseline for robustness research of CICL.