  In this work, we explore the usage of the Frequency Transformation for
reducing the domain shift between the source and target domain (e.g., synthetic
image and real image respectively) towards solving the Domain Adaptation task.
Most of the Unsupervised Domain Adaptation (UDA) algorithms focus on reducing
the global domain shift between labelled source and unlabelled target domains
by matching the marginal distributions under a small domain gap assumption. UDA
performance degrades for the cases where the domain gap between source and
target distribution is large. In order to bring the source and the target
domains closer, we propose a novel approach based on traditional image
processing technique Class Aware Frequency Transformation (CAFT) that utilizes
pseudo label based class consistent low-frequency swapping for improving the
overall performance of the existing UDA algorithms. The proposed approach, when
compared with the state-of-the-art deep learning based methods, is
computationally more efficient and can easily be plugged into any existing UDA
algorithm to improve its performance. Additionally, we introduce a novel
approach based on absolute difference of top-2 class prediction probabilities
(ADT2P) for filtering target pseudo labels into clean and noisy sets. Samples
with clean pseudo labels can be used to improve the performance of unsupervised
learning algorithms. We name the overall framework as CAFT++. We evaluate the
same on the top of different UDA algorithms across many public domain
adaptation datasets. Our extensive experiments indicate that CAFT++ is able to
achieve significant performance gains across all the popular benchmarks.
