1,206 research outputs found
Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment
Unsupervised domain adaptation is effective in leveraging the rich
information from the source domain to the unsupervised target domain. Though
deep learning and adversarial strategy make an important breakthrough in the
adaptability of features, there are two issues to be further explored. First,
the hard-assigned pseudo labels on the target domain are risky to the intrinsic
data structure. Second, the batch-wise training manner in deep learning limits
the description of the global structure. In this paper, a Riemannian manifold
learning framework is proposed to achieve transferability and discriminability
consistently. As to the first problem, this method establishes a probabilistic
discriminant criterion on the target domain via soft labels. Further, this
criterion is extended to a global approximation scheme for the second issue;
such approximation is also memory-saving. The manifold metric alignment is
exploited to be compatible with the embedding space. A theoretical error bound
is derived to facilitate the alignment. Extensive experiments have been
conducted to investigate the proposal and results of the comparison study
manifest the superiority of consistent manifold learning framework.Comment: Accepted to AAAI 2020. Code available:
\<https://github.com/LavieLuo/DRMEA
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Unsupervised Domain Adaptation via Discriminative Manifold Propagation
Unsupervised domain adaptation is effective in leveraging rich information
from a labeled source domain to an unlabeled target domain. Though deep
learning and adversarial strategy made a significant breakthrough in the
adaptability of features, there are two issues to be further studied. First,
hard-assigned pseudo labels on the target domain are arbitrary and error-prone,
and direct application of them may destroy the intrinsic data structure.
Second, batch-wise training of deep learning limits the characterization of the
global structure. In this paper, a Riemannian manifold learning framework is
proposed to achieve transferability and discriminability simultaneously. For
the first issue, this framework establishes a probabilistic discriminant
criterion on the target domain via soft labels. Based on pre-built prototypes,
this criterion is extended to a global approximation scheme for the second
issue. Manifold metric alignment is adopted to be compatible with the embedding
space. The theoretical error bounds of different alignment metrics are derived
for constructive guidance. The proposed method can be used to tackle a series
of variants of domain adaptation problems, including both vanilla and partial
settings. Extensive experiments have been conducted to investigate the method
and a comparative study shows the superiority of the discriminative manifold
learning framework.Comment: To be published in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Kernel Manifold Alignment
We introduce a kernel method for manifold alignment (KEMA) and domain
adaptation that can match an arbitrary number of data sources without needing
corresponding pairs, just few labeled examples in all domains. KEMA has
interesting properties: 1) it generalizes other manifold alignment methods, 2)
it can align manifolds of very different complexities, performing a sort of
manifold unfolding plus alignment, 3) it can define a domain-specific metric to
cope with multimodal specificities, 4) it can align data spaces of different
dimensionality, 5) it is robust to strong nonlinear feature deformations, and
6) it is closed-form invertible which allows transfer across-domains and data
synthesis. We also present a reduced-rank version for computational efficiency
and discuss the generalization performance of KEMA under Rademacher principles
of stability. KEMA exhibits very good performance over competing methods in
synthetic examples, visual object recognition and recognition of facial
expressions tasks
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