  As deep learning applications extensively increase by leaps and bounds, their
interpretability has become increasingly prominent. As a universal property,
chirality exists widely in nature, and applying it to the explanatory research
of deep learning may be helpful to some extent. Inspired by a recent study that
used CNN (convolutional neural network), which applied visual chirality, to
distinguish whether an image is flipped or not. In this paper, we study feature
chirality innovatively, which shows how the statistics of deep learning models'
feature data are changed by training. We rethink the feature-level chirality
property, propose the feature chirality, and give the measure. Our analysis of
feature chirality on AlexNet, VGG, and ResNet reveals similar but surprising
results, including the prevalence of feature chirality in these models, the
initialization methods of the models do not affect feature chirality. Our work
shows that feature chirality implies model evaluation, interpretability of the
model, and model parameters optimization.
