Oliver Wang: h-index, Total Citations, and Citation Map
Oliver Wang's h-index is 61 (126 i10-index, 53,050+ total citations across 5+ publications) according to Google Scholar as of May 2026. Oliver Wang is affiliated with Google DeepMind.
Oliver Wang is a researcher affiliated with Google DeepMind, specializing in Computer Vision, Machine Learning, Image Processing. Their work has been cited 53,050 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Oliver Wang's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 61
- i10-Index
- 126
- Total Citations
- 53,050
- Citing Countries
- 10
As of May 2026.
Oliver Wang has an h-index of 61 and 53,050 total citations across 5 publications, with research cited by institutions in 10 countries.
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We've mapped 5,000 of 53,050 citations for Oliver Wang
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Top Cited Works
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
201821,275
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Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher established deep neural network features as a robust perceptual metric, fundamentally shifting how image similarity is quantified in computer vision.
The researcher advanced multimodal image-to-image translation, establishing a foundational framework for generating diverse outputs from single inputs, as evidenced by a seminal NIPS 2017 paper with over 2,000 citations.
The researcher established a foundational benchmark for detecting CNN-generated images, demonstrating their initial detectability while highlighting the transient nature of such vulnerabilities in computer vision security.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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