Ronald Kemker
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 1%
- Media Technology top 1%
- Electrical and Electronic Engineering
- Cognitive Neuroscience top 10%
- Co-authors
- Christopher KananGerman I. ParisiStefan WermterJose L. PartCarl SalvaggioTyler L. HayesNathan D. Cahill
- Topics
- Domain Adaptation and Few-Shot Learning (5 papers)Advanced Image and Video Retrieval Techniques (4 papers)Remote-Sensing Image Classification (4 papers)
- Journals
- IEEE Transactions on Geoscience and Remote SensingISPRS Journal of Photogrammetry and Remote SensingNeural Networks
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Ronald Kemker
8 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 153
- Artificial Intelligence 1.5k
- Computer Vision and Pattern Recognition 916
- Media Technology 409
- Electrical and Electronic Engineering 207
- Cognitive Neuroscience 143
Countries citing papers authored by Ronald Kemker
This map shows the geographic impact of Ronald Kemker's research. 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 Ronald Kemker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ronald Kemker more than expected).
Fields of papers citing papers by Ronald Kemker
This network shows the impact of papers produced by Ronald Kemker. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ronald Kemker. The network helps show where Ronald Kemker may publish in the future.
Co-authorship network of co-authors of Ronald Kemker
This figure shows the co-authorship network connecting the top 25 collaborators of Ronald Kemker. A scholar is included among the top collaborators of Ronald Kemker based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ronald Kemker. Ronald Kemker is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Continual lifelong learning with neural networks: A reviewbreakdown → | 1665 |
| 2 | Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learningbreakdown → | 420 |
| 3 | 12 | |
| 4 | Measuring Catastrophic Forgetting in Neural Networksbreakdown → | 312 |
| 5 | 51 | |
| 6 | Deep Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Imagery. | 3 |
| 7 | FearNet: Brain-Inspired Model for Incremental Learning | 28 |
| 8 | 99 |
About Ronald Kemker
Ronald Kemker is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 8 papers that have together received 2.6k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Advanced Image and Video Retrieval Techniques (4 papers) and Remote-Sensing Image Classification (4 papers). The work is most often cited by research in Media Technology (409 citations), Artificial Intelligence (1.5k citations) and Computer Vision and Pattern Recognition (916 citations). Ronald Kemker has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Christopher Kanan, German I. Parisi, Stefan Wermter, Jose L. Part, Carl Salvaggio, Tyler L. Hayes and Nathan D. Cahill. Their work appears in journals such as IEEE Transactions on Geoscience and Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing and Neural Networks.
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.