3 research outputs found
Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) aims to adapt models trained from a
labeled source domain to a different but related target domain, from which
unlabeled data and a small set of labeled data are provided. Current methods
that treat source and target supervision without distinction overlook their
inherent discrepancy, resulting in a source-dominated model that has not
effectively use the target supervision. In this paper, we argue that the
labeled target data needs to be distinguished for effective SSDA, and propose
to explicitly decompose the SSDA task into two sub-tasks: a semi-supervised
learning (SSL) task in the target domain and an unsupervised domain adaptation
(UDA) task across domains. By doing so, the two sub-tasks can better leverage
the corresponding supervision and thus yield very different classifiers. To
integrate the strengths of the two classifiers, we apply the well-established
co-training framework, in which the two classifiers exchange their high
confident predictions to iteratively "teach each other" so that both
classifiers can excel in the target domain. We call our approach Deep
Co-training with Task decomposition (DeCoTa). DeCoTa requires no adversarial
training and is easy to implement. Moreover, DeCoTa is well-founded on the
theoretical condition of when co-training would succeed. As a result, DeCoTa
achieves state-of-the-art results on several SSDA datasets, outperforming the
prior art by a notable 4% margin on DomainNet
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
Data-driven based approaches, in spite of great success in many tasks, have
poor generalization when applied to unseen image domains, and require expensive
cost of annotation especially for dense pixel prediction tasks such as semantic
segmentation. Recently, both unsupervised domain adaptation (UDA) from large
amounts of synthetic data and semi-supervised learning (SSL) with small set of
labeled data have been studied to alleviate this issue. However, there is still
a large gap on performance compared to their supervised counterparts. We focus
on a more practical setting of semi-supervised domain adaptation (SSDA) where
both a small set of labeled target data and large amounts of labeled source
data are available. To address the task of SSDA, a novel framework based on
dual-level domain mixing is proposed. The proposed framework consists of three
stages. First, two kinds of data mixing methods are proposed to reduce domain
gap in both region-level and sample-level respectively. We can obtain two
complementary domain-mixed teachers based on dual-level mixed data from
holistic and partial views respectively. Then, a student model is learned by
distilling knowledge from these two teachers. Finally, pseudo labels of
unlabeled data are generated in a self-training manner for another few rounds
of teachers training. Extensive experimental results have demonstrated the
effectiveness of our proposed framework on synthetic-to-real semantic
segmentation benchmarks.Comment: CVPR202
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a
related but different well-labeled source domain to a new unlabeled target
domain. Most existing UDA methods require access to the source data, and thus
are not applicable when the data are confidential and not shareable due to
privacy concerns. This paper aims to tackle a realistic setting with only a
classification model available trained over, instead of accessing to, the
source data. To effectively utilize the source model for adaptation, we propose
a novel approach called Source HypOthesis Transfer (SHOT), which learns the
feature extraction module for the target domain by fitting the target data
features to the frozen source classification module (representing
classification hypothesis). Specifically, SHOT exploits both information
maximization and self-supervised learning for the feature extraction module
learning to ensure the target features are implicitly aligned with the features
of unseen source data via the same hypothesis. Furthermore, we propose a new
labeling transfer strategy, which separates the target data into two splits
based on the confidence of predictions (labeling information), and then employ
semi-supervised learning to improve the accuracy of less-confident predictions
in the target domain. We denote labeling transfer as SHOT++ if the predictions
are obtained by SHOT. Extensive experiments on both digit classification and
object recognition tasks show that SHOT and SHOT++ achieve results surpassing
or comparable to the state-of-the-arts, demonstrating the effectiveness of our
approaches for various visual domain adaptation problems. Code is available at
\url{https://github.com/tim-learn/SHOT-plus}.Comment: TPAMI 2021. More interesting results are further shown in
https://github.com/tim-learn/SHOT-plus/blob/master/supp/shot%2B%2B_supp.pdf.
arXiv admin note: text overlap with arXiv:2002.0854