464 research outputs found
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
Casting a BAIT for Offline and Online Source-free Domain Adaptation
We address the source-free domain adaptation (SFDA) problem, where only the
source model is available during adaptation to the target domain. We consider
two settings: the offline setting where all target data can be visited multiple
times (epochs) to arrive at a prediction for each target sample, and the online
setting where the target data needs to be directly classified upon arrival.
Inspired by diverse classifier based domain adaptation methods, in this paper
we introduce a second classifier, but with another classifier head fixed. When
adapting to the target domain, the additional classifier initialized from
source classifier is expected to find misclassified features. Next, when
updating the feature extractor, those features will be pushed towards the right
side of the source decision boundary, thus achieving source-free domain
adaptation. Experimental results show that the proposed method achieves
competitive results for offline SFDA on several benchmark datasets compared
with existing DA and SFDA methods, and our method surpasses by a large margin
other SFDA methods under online source-free domain adaptation setting
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