2,192 research outputs found
Co-regularized Alignment for Unsupervised Domain Adaptation
Deep neural networks, trained with large amount of labeled data, can fail to
generalize well when tested with examples from a \emph{target domain} whose
distribution differs from the training data distribution, referred as the
\emph{source domain}. It can be expensive or even infeasible to obtain required
amount of labeled data in all possible domains. Unsupervised domain adaptation
sets out to address this problem, aiming to learn a good predictive model for
the target domain using labeled examples from the source domain but only
unlabeled examples from the target domain. Domain alignment approaches this
problem by matching the source and target feature distributions, and has been
used as a key component in many state-of-the-art domain adaptation methods.
However, matching the marginal feature distributions does not guarantee that
the corresponding class conditional distributions will be aligned across the
two domains. We propose co-regularized domain alignment for unsupervised domain
adaptation, which constructs multiple diverse feature spaces and aligns source
and target distributions in each of them individually, while encouraging that
alignments agree with each other with regard to the class predictions on the
unlabeled target examples. The proposed method is generic and can be used to
improve any domain adaptation method which uses domain alignment. We
instantiate it in the context of a recent state-of-the-art method and observe
that it provides significant performance improvements on several domain
adaptation benchmarks.Comment: NIPS 2018 accepted versio
Optimal Transport for Domain Adaptation
Domain adaptation from one data space (or domain) to another is one of the
most challenging tasks of modern data analytics. If the adaptation is done
correctly, models built on a specific data space become more robust when
confronted to data depicting the same semantic concepts (the classes), but
observed by another observation system with its own specificities. Among the
many strategies proposed to adapt a domain to another, finding a common
representation has shown excellent properties: by finding a common
representation for both domains, a single classifier can be effective in both
and use labelled samples from the source domain to predict the unlabelled
samples of the target domain. In this paper, we propose a regularized
unsupervised optimal transportation model to perform the alignment of the
representations in the source and target domains. We learn a transportation
plan matching both PDFs, which constrains labelled samples in the source domain
to remain close during transport. This way, we exploit at the same time the few
labeled information in the source and the unlabelled distributions observed in
both domains. Experiments in toy and challenging real visual adaptation
examples show the interest of the method, that consistently outperforms state
of the art approaches
Joint Distribution Optimal Transportation for Domain Adaptation
This paper deals with the unsupervised domain adaptation problem, where one
wants to estimate a prediction function in a given target domain without
any labeled sample by exploiting the knowledge available from a source domain
where labels are known. Our work makes the following assumption: there exists a
non-linear transformation between the joint feature/label space distributions
of the two domain and . We propose a solution of
this problem with optimal transport, that allows to recover an estimated target
by optimizing simultaneously the optimal coupling
and . We show that our method corresponds to the minimization of a bound on
the target error, and provide an efficient algorithmic solution, for which
convergence is proved. The versatility of our approach, both in terms of class
of hypothesis or loss functions is demonstrated with real world classification
and regression problems, for which we reach or surpass state-of-the-art
results.Comment: Accepted for publication at NIPS 201
Joint cross-domain classification and subspace learning for unsupervised adaptation
Domain adaptation aims at adapting the knowledge acquired on a source domain
to a new different but related target domain. Several approaches have
beenproposed for classification tasks in the unsupervised scenario, where no
labeled target data are available. Most of the attention has been dedicated to
searching a new domain-invariant representation, leaving the definition of the
prediction function to a second stage. Here we propose to learn both jointly.
Specifically we learn the source subspace that best matches the target subspace
while at the same time minimizing a regularized misclassification loss. We
provide an alternating optimization technique based on stochastic sub-gradient
descent to solve the learning problem and we demonstrate its performance on
several domain adaptation tasks.Comment: Paper is under consideration at Pattern Recognition Letter
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
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