DC-FUDA: Improving deep clustering via fully unsupervised domain adaptation-Reference-Cited by-同舟云学术

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DC-FUDA: Improving deep clustering via fully unsupervised domain adaptation

Published:2023-03 Issue: Volume:526 Page:109-120
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Yang Zhimeng,Ren Yazhou,Wu Zirui,Zeng Ming,Xu Jie,Yang Yang,Pu Xiaorong,Yu Philip S.,He Lifang

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference82 articles.

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2. Generic network for domain adaptation based on self-supervised learning and deep clustering;Baffour;Neurocomputing,2022

3. K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, D. Krishnan, Unsupervised pixel-level domain adaptation with generative adversarial networks, in: CVPR, 2017, pp. 3722–3731.

4. Semi-supervised information-maximization clustering;Calandriello;Neural Netw.,2014

5. F.M. Carlucci, L. Porzi, B. Caputo, E. Ricci, S.R. Bulò, Just dial: Domain alignment layers for unsupervised domain adaptation, in: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (Eds.), ICIAP, 2017, pp. 357–369.

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1. Rectifying self-training with neighborhood consistency and proximity for source-free domain adaptation;Neurocomputing;2024-11

2. Unsupervised cross domain semantic segmentation with mutual refinement and information distillation;Neurocomputing;2024-06

3. nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering;2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT);2023-11-10

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