Unsupervised Learning For Segmentation
Unsupervised Learning for Segmentation is a machine learning approach that partitions data into distinct groups or regions based on inherent patterns and similarities without relying on labeled training examples, thereby distinguishing itself from supervised segmentation methods by eliminating the need for prior ground-truth annotations.
305
Publications
21.2K
Citations
885
Authors
396
Institutions
Publications per year
2017–2026
98
Parents
305
1–5 of 305
885
Centre National de la Recherche Scientifique, France
10 publications · 545 citations
10
h-index
5
h-index
5
h-index
4
h-index
Centre National de la Recherche Scientifique, France
3 publications · 110 citations
3
h-index
1–5 of 885
396

Centre National de la Recherche Scientifique
Paris, France
12 h-index
29 publications
1.1K citations
University of California, Berkeley
Berkeley, United States
7 h-index
19 publications
1.6K citations

Xidian University
Xi'an, China
6 h-index
25 publications
888 citations
University of South Florida
Tampa, United States
5 h-index
20 publications
3.1K citations

Johns Hopkins University
Baltimore, United States
5 h-index
12 publications
691 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
5 h-index
9 publications
1.2K citations

International Computer Science Institute
Berkeley, United States
4 h-index
8 publications
321 citations

Shanghai Jiao Tong University
Shanghai, China
4 h-index
5 publications
196 citations
1–8 of 396
14 publications · 1.4K citations
14
h-index
Repository · 11 publications · 406 citations
11
h-index
7 publications · 252 citations
7
h-index
7 publications · 690 citations
7
h-index
7 publications · 515 citations
7
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1–5