  In recent years, convolutional neural network-based single image adverse
weather removal methods have achieved significant performance improvements on
many benchmark datasets. However, these methods require large amounts of
clean-weather degraded image pairs for training, which is often difficult to
obtain in practice. Although various weather degradation synthesis methods
exist in the literature, the use of synthetically generated weather degraded
images often results in sub-optimal performance on the real weather degraded
images due to the domain gap between synthetic and real-world images. To deal
with this problem, various semi-supervised restoration (SSR) methods have been
proposed for deraining or dehazing which learn to restore the clean image using
synthetically generated datasets while generalizing better using unlabeled
real-world images. The performance of a semi-supervised method is essentially
based on the quality of the unlabeled data. In particular, if the unlabeled
data characteristics are very different from that of the labeled data, then the
performance of a semi-supervised method degrades significantly. We
theoretically study the effect of unlabeled data on the performance of an SSR
method and develop a technique that rejects the unlabeled images that degrade
the performance. Extensive experiments and ablation study show that the
proposed sample rejection method increases the performance of existing SSR
deraining and dehazing methods significantly. Code is available at
:https://github.com/rajeevyasarla/ART-SS
