Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
Impact in
Classified as
- Authors
- Harish G. Ramaswamy
In The Last Decade
doi.org/10.1109/wacv45572.2020.9093360 →Countries where authors are citing Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
This map shows the geographic impact of Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization more than expected).
Fields of papers citing Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
This network shows the impact of Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization.
About Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
This paper, published in 2020, received 279 indexed citations . Written by Harish G. Ramaswamy covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (184 citations), Computer Vision and Pattern Recognition (130 citations), Radiology, Nuclear Medicine and Imaging (35 citations), Biophysics (25 citations) and Media Technology (18 citations).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/wacv45572.2020.9093360.