4.7 Article

ACDC: Online unsupervised cross-domain adaptation

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

Automatic online multi-source domain adaptation

Xie Renchunzi et al.

Summary: Despite the challenging problem of knowledge transfer across multiple streaming processes, the proposed automatic online multi-source domain adaptation (AOMSDA) technique effectively addresses the issue by integrating a central moment discrepancy (CMD)-based regularizer under a coupled generative and discriminative approach of denoising autoencoder (DAE). Numerical studies show that AOMSDA outperforms its counterparts in 5 out of 8 cases, with ablation studies highlighting the advantages of each learning component. AOMSDA is also generalizable for any number of source streams and the source code is publicly available.

INFORMATION SCIENCES (2022)

Article Computer Science, Artificial Intelligence

Challenging tough samples in unsupervised domain adaptation

Lin Zuo et al.

Summary: This paper proposes three novel ideas for domain adaptation: splitting target samples into easy and tough ones; deploying different strategies for samples with different adaptation difficulties; leveraging easy samples to facilitate tough ones. The novel approach challenging tough sample networks (CTSN) is presented to practice these ideas and successfully handle tough samples.

PATTERN RECOGNITION (2021)

Article Computer Science, Artificial Intelligence

Online deep transferable dictionary learning

Sheng Wu et al.

Summary: This study introduces an online deep transferable dictionary learning method to address data cluster discrepancies between incoming unlabeled data and older labeled data. By utilizing a two-level affiliation regularizer to reveal local and global affiliations, it establishes a knowledge transfer pipeline and demonstrates superiority in the online setting.

PATTERN RECOGNITION (2021)

Article Computer Science, Artificial Intelligence

Learning adaptive geometry for unsupervised domain adaptation

Baoyao Yang et al.

Summary: This paper proposes a method to address dataset bias issues by aligning data representations and geometries to handle the problem of inconsistent geometries between source and target domains. By learning adaptive geometry and integrating adversarial learning techniques, a geometry-aware dual-stream network is developed to learn geometry-aligned representations.

PATTERN RECOGNITION (2021)

Article Computer Science, Artificial Intelligence

Discriminative feature alignment: Improving transferability of unsupervised domain adaptation by Gaussian-guided latent alignment

Jing Wang et al.

Summary: In this paper, a Gaussian-guided latent alignment approach is proposed for unsupervised domain adaptation to tackle the issue of feature alignment. By constructing a common feature space and aligning the latent feature distributions of two domains, better feature alignment is achieved. Extensive evaluations on nine benchmark datasets confirm the superior performance of the proposed method.

PATTERN RECOGNITION (2021)

Article Computer Science, Artificial Intelligence

Learning Robust Feature Transformation for Domain Adaptation

Wei Wang et al.

Summary: The paper proposes a novel Robust Transfer Feature Learning (RTFL) method to enhance the robustness of domain adaptation by reducing the distribution difference between two domains. RTFL learns a shared transformation by detecting and neglecting contaminated target points, reconstructing clean target points, and incorporating a relative entropy based regularization for theoretical advantages. Extensive experiments demonstrate the superiority of the proposed method.

PATTERN RECOGNITION (2021)

Article Computer Science, Artificial Intelligence

ExprADA: Adversarial domain adaptation for facial expression analysis

Behzad Bozorgtabar et al.

PATTERN RECOGNITION (2020)

Article Computer Science, Artificial Intelligence

Semi-supervised transfer subspace for domain adaptation

Luis A. M. Pereira et al.

PATTERN RECOGNITION (2018)

Article Computer Science, Artificial Intelligence

Characterizing concept drift

Geoffrey I. Webb et al.

DATA MINING AND KNOWLEDGE DISCOVERY (2016)

Article Computer Science, Artificial Intelligence

Online and Non-Parametric Drift Detection Methods Based on Hoeffding's Bounds

Isvani Frias-Blanco et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2015)

Article Computer Science, Artificial Intelligence

A survey of multi-source domain adaptation

Shiliang Sun et al.

INFORMATION FUSION (2015)

Article Computer Science, Theory & Methods

A Survey on Concept Drift Adaptation

Joao Gama et al.

ACM COMPUTING SURVEYS (2014)

Article Computer Science, Artificial Intelligence

Online Transfer Learning

Peilin Zhao et al.

ARTIFICIAL INTELLIGENCE (2014)

Article Computer Science, Artificial Intelligence

Event labeling combining ensemble detectors and background knowledge

Hadi Fanaee-T et al.

PROGRESS IN ARTIFICIAL INTELLIGENCE (2014)

Article Computer Science, Artificial Intelligence

On evaluating stream learning algorithms

Joao Gama et al.

MACHINE LEARNING (2013)

Article Computer Science, Artificial Intelligence

Facing the reality of data stream classification: coping with scarcity of labeled data

Mohammad M. Masud et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2012)

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Computer Science, Artificial Intelligence

A theory of learning from different domains

Shai Ben-David et al.

MACHINE LEARNING (2010)