Anindya Sarkar
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Media Technology top 10%
- Oncology
- Co-authors
- B.S. ManjunathKien NguyenAnil K. JainLakshmanan NatarajKaushal SolankiUpamanyu MadhowVineeth N BalasubramanianAmbuj K. Singh
- Topics
- AI in cancer detection (5 papers)Advanced Steganography and Watermarking Techniques (5 papers)Digital Media Forensic Detection (5 papers)
- Journals
- IEEE Transactions on Medical ImagingDalton TransactionsIEEE Transactions on Circuits and Systems for Video Technology
- Partner nations
- United StatesIndiaSouth Korea
In The Last Decade
Anindya Sarkar
19 papers receiving 423 citations
Peers
Comparison fields: 5 of 53
- Computer Vision and Pattern Recognition 287
- Artificial Intelligence 183
- Radiology, Nuclear Medicine and Imaging 84
- Media Technology 39
- Oncology 38
Countries citing papers authored by Anindya Sarkar
This map shows the geographic impact of Anindya Sarkar'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 Anindya Sarkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anindya Sarkar more than expected).
Fields of papers citing papers by Anindya Sarkar
This network shows the impact of papers produced by Anindya Sarkar. 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 Anindya Sarkar. The network helps show where Anindya Sarkar may publish in the future.
Co-authorship network of co-authors of Anindya Sarkar
This figure shows the co-authorship network connecting the top 25 collaborators of Anindya Sarkar. A scholar is included among the top collaborators of Anindya Sarkar 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 Anindya Sarkar. Anindya Sarkar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 24 | |
| 2 | 4 | |
| 3 | Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach | 1 |
| 4 | 33 | |
| 5 | 14 | |
| 6 | 17 | |
| 7 | 3 | |
| 8 | 50 | |
| 9 | 16 | |
| 10 | 62 | |
| 11 | 32 | |
| 12 | 16 | |
| 13 | 25 | |
| 14 | 58 | |
| 15 | 41 | |
| 16 | 0 | |
| 17 | UThe Vision Research Lab of UCSB at TRECVID 2007. | 1 |
| 18 | 11 | |
| 19 | 13 | |
| 20 | 19 |
About Anindya Sarkar
Anindya Sarkar is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Artificial Intelligence, having authored 20 papers that have together received 440 indexed citations. Recurring topics across this work include AI in cancer detection (5 papers), Advanced Steganography and Watermarking Techniques (5 papers) and Digital Media Forensic Detection (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (287 citations), Biophysics (35 citations) and Artificial Intelligence (183 citations). Anindya Sarkar has collaborated with scholars based in United States, India and South Korea. Frequent co-authors include B.S. Manjunath, Kien Nguyen, Anil K. Jain, Lakshmanan Nataraj, Kaushal Solanki, Upamanyu Madhow, Vineeth N Balasubramanian, Ambuj K. Singh, Uday Maitra and Ramesh Kandanelli. Their work appears in journals such as IEEE Transactions on Medical Imaging, Dalton Transactions and IEEE Transactions on Circuits and Systems for Video Technology.
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.