Eric Tzeng
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- Advanced Image and Video Retrieval Techniques 8
- Multimodal Machine Learning Applications 6
- Advanced Neural Network Applications 3
- Image Retrieval and Classification Techniques 2
- Artificial Intelligence top 0.2%
- Domain Adaptation and Few-Shot Learning 7
- Adversarial Robustness in Machine Learning 2
- Media Technology top 1%
- Signal Processing top 2%
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- Robot Manipulation and Learning 2
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- Robotics and Sensor-Based Localization 2
- Co-authors
- Trevor DarrellJudy HoffmanKate SaenkoJeff DonahueYangqing JiaOriol VinyalsNing ZhangSergio Guadarrama
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Zenodo (CERN European Organization for Nuclear Research) (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- United StatesPolandChina
In The Last Decade
Eric Tzeng
17 papers receiving 5.8k citations
Hit Papers
Peers
Comparison fields: 5 of 161
- Computer Vision and Pattern Recognition 3.7k
- Artificial Intelligence 3.6k
- Media Technology 427
- Signal Processing 315
- Radiology, Nuclear Medicine and Imaging 624
Countries citing papers authored by Eric Tzeng
This map shows the geographic impact of Eric Tzeng'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 Eric Tzeng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Tzeng more than expected).
Fields of papers citing papers by Eric Tzeng
This network shows the impact of papers produced by Eric Tzeng. 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 Eric Tzeng. The network helps show where Eric Tzeng may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Eric Tzeng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 0 | |
| 2 | Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck | 2021 | 1 |
| 3 | 2019 | 23 | |
| 4 | Adapting to Continuously Shifting Domains | 2018 | 32 |
| 5 | Adversarial Discriminative Domain Adaptation (workshop extended abstract). | 2017 | 2 |
| 6 | 2017 | 7 | |
| 7 | 2017 | 36 | |
| 8 | Adversarial Discriminative Domain Adaptationbreakdown → | 2017 | 2951 |
| 9 | Large scale visual recognition through adaptation using joint representation and multiple instance learning | 2016 | 8 |
| 10 | 2015 | 2 | |
| 11 | Simultaneous Deep Transfer Across Domains and Tasksbreakdown → | 2015 | 690 |
| 12 | 2014 | 24 | |
| 13 | 2014 | 125 | |
| 14 | From Large-Scale Object Classifiers to Large-Scale Object Detectors: An Adaptation Approach | 2014 | 2 |
| 15 | 2014 | 40 | |
| 16 | One-Shot Adaptation of Supervised Deep Convolutional Models | 2013 | 10 |
| 17 | 2013 | 21 | |
| 18 | DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognitionbreakdown → | 2013 | 2085 |
About Eric Tzeng
Eric Tzeng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Critical Care and Intensive Care Medicine, having authored 18 papers that have together received 6.1k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (8 papers), Domain Adaptation and Few-Shot Learning (7 papers), Multimodal Machine Learning Applications (6 papers), Advanced Neural Network Applications (3 papers), Image Retrieval and Classification Techniques (2 papers), Robot Manipulation and Learning (2 papers), Adversarial Robustness in Machine Learning (2 papers) and Robotics and Sensor-Based Localization (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.7k citations), Artificial Intelligence (3.6k citations) and Media Technology (427 citations). Eric Tzeng has collaborated with scholars based in United States, Poland and China. Frequent co-authors include Trevor Darrell, Judy Hoffman, Kate Saenko, Jeff Donahue, Yangqing Jia, Oriol Vinyals, Ning Zhang, Sergio Guadarrama, Ronghang Hu and Ross Girshick. Their work appears in journals such as Proceedings of the National Academy of Sciences, Zenodo (CERN European Organization for Nuclear Research), arXiv (Cornell University), International Conference on Learning Representations and Cureus.
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.