Visual Explanations
Visual Explanations is a methodological framework within explainable machine learning and explainable recommendation that employs visual representations to make model decisions interpretable by explicitly highlighting salient input features or decision rationales through graphical or imagery-based outputs.
568
Publications
73.8K
Citations
1.6K
Authors
531
Institutions
Publications per year
2017–2026
460
568
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das et al. · 2017 · 21.6K citations
Yukun Zhu, Ryan Kiros, Rich Zemel et al. · 2015 · 2.1K citations · Full text
1–5 of 568
1.6K
10
h-index
Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Germany
10 publications · 6K citations
10
h-index
7
h-index
7
h-index
7
h-index
1–5 of 1.6K
531
Georgia Institute of Technology
Atlanta, United States
12 h-index
49 publications
166.4K citations
Carnegie Mellon University
Pittsburgh, United States
12 h-index
31 publications
5.2K citations
Technische Universität Berlin
Berlin, Germany
11 h-index
24 publications
23.9K citations
Korea University
Seoul, South Korea
10 h-index
18 publications
5.3K citations
University of California, Berkeley
Berkeley, United States
9 h-index
28 publications
2K citations
Tsinghua University
Beijing, China
8 h-index
16 publications
1.2K citations
Stanford University
Stanford, United States
8 h-index
11 publications
979 citations
Max Planck Institute for Informatics
Saarbrücken, Germany
8 h-index
13 publications
738 citations
1–8 of 531
Repository · 96 publications · 6.6K citations
38
h-index
Conference · 13 publications · 445 citations
13
h-index
10 publications · 348 citations
11
h-index
TVCG · 7 publications · 949 citations
8
h-index
TPAMI · 4 publications · 112 citations
6
h-index
1–5