4 research outputs found
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration
In conventional domain adaptation, a critical assumption is that there exists
a fully labeled domain (source) that contains the same label space as another
unlabeled or scarcely labeled domain (target). However, in the real world,
there often exist application scenarios in which both domains are partially
labeled and not all classes are shared between these two domains. Thus, it is
meaningful to let partially labeled domains learn from each other to classify
all the unlabeled samples in each domain under an open-set setting. We consider
this problem as weakly supervised open-set domain adaptation. To address this
practical setting, we propose the Collaborative Distribution Alignment (CDA)
method, which performs knowledge transfer bilaterally and works collaboratively
to classify unlabeled data and identify outlier samples. Extensive experiments
on the Office benchmark and an application on person reidentification show that
our method achieves state-of-the-art performance.Comment: CVPR 201
Sparsely-Labeled Source Assisted Domain Adaptation
Domain Adaptation (DA) aims to generalize the classifier learned from the
source domain to the target domain. Existing DA methods usually assume that
rich labels could be available in the source domain. However, there are usually
a large number of unlabeled data but only a few labeled data in the source
domain, and how to transfer knowledge from this sparsely-labeled source domain
to the target domain is still a challenge, which greatly limits their
application in the wild. This paper proposes a novel Sparsely-Labeled Source
Assisted Domain Adaptation (SLSA-DA) algorithm to address the challenge with
limited labeled source domain samples. Specifically, due to the label scarcity
problem, the projected clustering is conducted on both the source and target
domains, so that the discriminative structures of data could be leveraged
elegantly. Then the label propagation is adopted to propagate the labels from
those limited labeled source samples to the whole unlabeled data progressively,
so that the cluster labels are revealed correctly. Finally, we jointly align
the marginal and conditional distributions to mitigate the cross-domain
mismatch problem, and optimize those three procedures iteratively. However, it
is nontrivial to incorporate those three procedures into a unified optimization
framework seamlessly since some variables to be optimized are implicitly
involved in their formulas, thus they could not promote to each other.
Remarkably, we prove that the projected clustering and conditional distribution
alignment could be reformulated as different expressions, thus the implicit
variables are revealed in different optimization steps. As such, the variables
related to those three quantities could be optimized in a unified optimization
framework and facilitate to each other, to improve the recognition performance
obviously.Comment: 22 pages, 6 figures, submitted to the Pattern Recognitio
Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation
In this paper, we introduce a collaborative training algorithm of balanced
random forests with convolutional neural networks for domain adaptation tasks.
In real scenarios, most domain adaptation algorithms face the challenges from
noisy, insufficient training data and open set categorization. In such cases,
conventional methods suffer from overfitting and fail to successfully transfer
the knowledge of the source to the target domain. To address these issues, the
following two techniques are proposed. First, we introduce the optimized
decision tree construction method with convolutional neural networks, in which
the data at each node are split into equal sizes while maximizing the
information gain. It generates balanced decision trees on deep features because
of the even-split constraint, which contributes to enhanced discrimination
power and reduced overfitting problem. Second, to tackle the domain
misalignment problem, we propose the domain alignment loss which penalizes
uneven splits of the source and target domain data. By collaboratively
optimizing the information gain of the labeled source data as well as the
entropy of unlabeled target data distributions, the proposed CoBRF algorithm
achieves significantly better performance than the state-of-the-art methods
Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
Unsupervised domain adaptation aims to leverage labeled data from a source
domain to learn a classifier for an unlabeled target domain. Among its many
variants, open set domain adaptation (OSDA) is perhaps the most challenging, as
it further assumes the presence of unknown classes in the target domain. In
this paper, we study OSDA with a particular focus on enriching its ability to
traverse across larger domain gaps. Firstly, we show that existing
state-of-the-art methods suffer a considerable performance drop in the presence
of larger domain gaps, especially on a new dataset (PACS) that we re-purposed
for OSDA. We then propose a novel framework to specifically address the larger
domain gaps. The key insight lies with how we exploit the mutually beneficial
information between two networks; (a) to separate samples of known and unknown
classes, (b) to maximize the domain confusion between source and target domain
without the influence of unknown samples. It follows that (a) and (b) will
mutually supervise each other and alternate until convergence. Extensive
experiments are conducted on Office-31, Office-Home, and PACS datasets,
demonstrating the superiority of our method in comparison to other
state-of-the-arts. Code available at
https://github.com/dongliangchang/Mutual-to-Separate