Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation
Gabriel Tjio Ping Liu Yawei Luo Chee Keong Kwoh Joey Zhou Tianyi
This work addresses the challenging domain adaptation setting in which
knowledge from the labelled source domain dataset is available only from the
pretrained black-box segmentation model. The pretrained model's predictions for
the target domain images are noisy because of the distributional differences
between the source domain data and the target domain data. Since the model's
predictions serve as pseudo labels during self-training, the noise in the
predictions impose an upper bound on model performance. Therefore, we propose a
simple yet novel image translation workflow, ReGEN, to address this problem.
ReGEN comprises an image-to-image translation network and a segmentation
network. Our workflow generates target-like images using the noisy predictions
from the original target domain images. These target-like images are
semantically consistent with the noisy model predictions and therefore can be
used to train the segmentation network. In addition to being semantically
consistent with the predictions from the original target domain images, the
generated target-like images are also stylistically similar to the target
domain images. This allows us to leverage the stylistic differences between the
target-like images and the target domain image as an additional source of
supervision while training the segmentation model. We evaluate our model with
two benchmark domain adaptation settings and demonstrate that our approach
performs favourably relative to recent state-of-the-art work. The source code
will be made available.