  The key procedure of haze image translation through adversarial training lies
in the disentanglement between the feature only involved in haze synthesis,
i.e.style feature, and the feature representing the invariant semantic content,
i.e. content feature. Previous methods separate content feature apart by
utilizing it to classify haze image during the training process. However, in
this paper we recognize the incompleteness of the content-style disentanglement
in such technical routine. The flawed style feature entangled with content
information inevitably leads the ill-rendering of the haze images. To address,
we propose a self-supervised style regression via stochastic linear
interpolation to reduce the content information in style feature. The ablative
experiments demonstrate the disentangling completeness and its superiority in
level-aware haze image synthesis. Moreover, the generated haze data are applied
in the testing generalization of vehicle detectors. Further study between
haze-level and detection performance shows that haze has obvious impact on the
generalization of the vehicle detectors and such performance degrading level is
linearly correlated to the haze-level, which, in turn, validates the
effectiveness of the proposed method.
