Pinghua Gong

Papers

Total Citations

104

H-Index

About

Pinghua Gong is a leading researcher in machine learning, with a focused expertise in few-shot learning—the challenge of enabling AI systems to learn and generalize from extremely limited data. His work addresses one of the most critical demarcations between artificial and human intelligence: the human ability to form robust concepts from just a handful of examples. Gong’s major contribution is his comprehensive survey on machine learning from few samples, published in 2023, which has already garnered 80 citations. This work systematically maps the landscape of few-shot learning, synthesizing key methods, benchmarks, and open challenges. An earlier version of this survey, released in 2020, also proved influential with 24 citations, demonstrating sustained interest in his synthesis of the field. By clarifying the core principles and progress in few-sample learning, Gong has provided an essential roadmap for researchers working to bridge the gap between data-hungry algorithms and human-like learning efficiency. His surveys serve as foundational references for anyone entering or advancing this rapidly evolving area of artificial intelligence.