27,484 research outputs found
Multi-step domain adaptation by adversarial attack to -divergence
Adversarial examples are transferable between different models. In our paper,
we propose to use this property for multi-step domain adaptation. In
unsupervised domain adaptation settings, we demonstrate that replacing the
source domain with adversarial examples to -divergence can improve source classifier accuracy on the target
domain. Our method can be connected to most domain adaptation techniques. We
conducted a range of experiments and achieved improvement in accuracy on Digits
and Office-Home datasets
MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation
Recent progresses in domain adaptive semantic segmentation demonstrate the
effectiveness of adversarial learning (AL) in unsupervised domain adaptation.
However, most adversarial learning based methods align source and target
distributions at a global image level but neglect the inconsistency around
local image regions. This paper presents a novel multi-level adversarial
network (MLAN) that aims to address inter-domain inconsistency at both global
image level and local region level optimally. MLAN has two novel designs,
namely, region-level adversarial learning (RL-AL) and co-regularized
adversarial learning (CR-AL). Specifically, RL-AL models prototypical regional
context-relations explicitly in the feature space of a labelled source domain
and transfers them to an unlabelled target domain via adversarial learning.
CR-AL fuses region-level AL and image-level AL optimally via mutual
regularization. In addition, we design a multi-level consistency map that can
guide domain adaptation in both input space (, image-to-image
translation) and output space (, self-training) effectively. Extensive
experiments show that MLAN outperforms the state-of-the-art with a large margin
consistently across multiple datasets.Comment: Submitted to P
Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation
Domain adaptation aims to transfer knowledge from a domain with adequate
labeled samples to a domain with scarce labeled samples. Prior research has
introduced various open set domain adaptation settings in the literature to
extend the applications of domain adaptation methods in real-world scenarios.
This paper focuses on the type of open set domain adaptation setting where the
target domain has both private ('unknown classes') label space and the shared
('known classes') label space. However, the source domain only has the 'known
classes' label space. Prevalent distribution-matching domain adaptation methods
are inadequate in such a setting that demands adaptation from a smaller source
domain to a larger and diverse target domain with more classes. For addressing
this specific open set domain adaptation setting, prior research introduces a
domain adversarial model that uses a fixed threshold for distinguishing known
from unknown target samples and lacks at handling negative transfers. We extend
their adversarial model and propose a novel adversarial domain adaptation model
with multiple auxiliary classifiers. The proposed multi-classifier structure
introduces a weighting module that evaluates distinctive domain characteristics
for assigning the target samples with weights which are more representative to
whether they are likely to belong to the known and unknown classes to encourage
positive transfers during adversarial training and simultaneously reduces the
domain gap between the shared classes of the source and target domains. A
thorough experimental investigation shows that our proposed method outperforms
existing domain adaptation methods on a number of domain adaptation datasets.Comment: Accepted in IEEE Transactions on Multimedia (in press), 202
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
Previous studies have shown that leveraging domain index can significantly
boost domain adaptation performance (arXiv:2007.01807, arXiv:2202.03628).
However, such domain indices are not always available. To address this
challenge, we first provide a formal definition of domain index from the
probabilistic perspective, and then propose an adversarial variational Bayesian
framework that infers domain indices from multi-domain data, thereby providing
additional insight on domain relations and improving domain adaptation
performance. Our theoretical analysis shows that our adversarial variational
Bayesian framework finds the optimal domain index at equilibrium. Empirical
results on both synthetic and real data verify that our model can produce
interpretable domain indices which enable us to achieve superior performance
compared to state-of-the-art domain adaptation methods. Code is available at
https://github.com/Wang-ML-Lab/VDI.Comment: ICLR 2023 Spotlight (notable-top-25%
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