90 research outputs found
Contrastive Domain Adaptation
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Deep Clustering: A Comprehensive Survey
Cluster analysis plays an indispensable role in machine learning and data
mining. Learning a good data representation is crucial for clustering
algorithms. Recently, deep clustering, which can learn clustering-friendly
representations using deep neural networks, has been broadly applied in a wide
range of clustering tasks. Existing surveys for deep clustering mainly focus on
the single-view fields and the network architectures, ignoring the complex
application scenarios of clustering. To address this issue, in this paper we
provide a comprehensive survey for deep clustering in views of data sources.
With different data sources and initial conditions, we systematically
distinguish the clustering methods in terms of methodology, prior knowledge,
and architecture. Concretely, deep clustering methods are introduced according
to four categories, i.e., traditional single-view deep clustering,
semi-supervised deep clustering, deep multi-view clustering, and deep transfer
clustering. Finally, we discuss the open challenges and potential future
opportunities in different fields of deep clustering
HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation
Minimizing the discrepancy of feature distributions between different domains
is one of the most promising directions in unsupervised domain adaptation. From
the perspective of distribution matching, most existing discrepancy-based
methods are designed to match the second-order or lower statistics, which
however, have limited expression of statistical characteristic for non-Gaussian
distributions. In this work, we explore the benefits of using higher-order
statistics (mainly refer to third-order and fourth-order statistics) for domain
matching. We propose a Higher-order Moment Matching (HoMM) method, and further
extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular,
our proposed HoMM can perform arbitrary-order moment tensor matching, we show
that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and
the second-order HoMM is equivalent to Correlation Alignment (CORAL). Moreover,
the third-order and the fourth-order moment tensor matching are expected to
perform comprehensive domain alignment as higher-order statistics can
approximate more complex, non-Gaussian distributions. Besides, we also exploit
the pseudo-labeled target samples to learn discriminative representations in
the target domain, which further improves the transfer performance. Extensive
experiments are conducted, showing that our proposed HoMM consistently
outperforms the existing moment matching methods by a large margin. Codes are
available at \url{https://github.com/chenchao666/HoMM-Master}Comment: Accept by AAAI-2020, codes are available at
https://github.com/chenchao666/HoMM-Maste
Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation
We address the Unsupervised Domain Adaptation (UDA) problem in image classification from a new perspective. In contrast to most existing works which either align the data distributions or learn domain-invariant features, we directly learn a unified classifier for both the source and target domains in the high-dimensional homogeneous feature space without explicit domain alignment. To this end, we employ the effective Selective Pseudo-Labelling (SPL) technique to take advantage of the unlabelled samples in the target domain. Surprisingly, data distribution discrepancy across the source and target domains can be well handled by a computationally simple classifier (e.g., a shallow Multi-Layer Perceptron) trained in the original feature space. Besides, we propose a novel generative model norm-AE to generate synthetic features for the target domain as a data augmentation strategy to enhance the classifier training. Experimental results on several benchmark datasets demonstrate the pseudo-labelling strategy itself can lead to comparable performance to many state-of-the-art methods whilst the use of norm-AE for feature augmentation can further improve the performance in most cases. As a result, our proposed methods (i.e. naiveSPL and norm-AE-SPL) can achieve comparable performance with state-of-the-art methods with the average accuracy of 93.4% and 90.4% on Office-Caltech and ImageCLEF-DA datasets, and achieve competitive performance on Digits, Office31 and Office-Home datasets with the average accuracy of 97.2%, 87.6% and 68.6% respectively
A Survey on Negative Transfer
Transfer learning (TL) tries to utilize data or knowledge from one or more
source domains to facilitate the learning in a target domain. It is
particularly useful when the target domain has few or no labeled data, due to
annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of
TL is not always guaranteed. Negative transfer (NT), i.e., the source domain
data/knowledge cause reduced learning performance in the target domain, has
been a long-standing and challenging problem in TL. Various approaches to
handle NT have been proposed in the literature. However, this filed lacks a
systematic survey on the formalization of NT, their factors and the algorithms
that handle NT. This paper proposes to fill this gap. First, the definition of
negative transfer is considered and a taxonomy of the factors are discussed.
Then, near fifty representative approaches for handling NT are categorized and
reviewed, from four perspectives: secure transfer, domain similarity
estimation, distant transfer and negative transfer mitigation. NT in related
fields, e.g., multi-task learning, lifelong learning, and adversarial attacks
are also discussed
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