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A survey on machine learning from few samples
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AI
DOI：10.1016/j.patcog.2023.109480.png)
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摘要
En 中文
The capability of learning and generalizing from very few samples successfully is a noticeable demarca-tion separating artificial intelligence and human intelligence. Despite the long history dated back to the early 20 0 0s and the widespread attention in recent years with booming deep learning, few surveys for few sample learning (FSL) are available. We extensively study almost all papers of FSL spanning from the 20 0 0s to now and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history and current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review their latest advances. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches.(c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Few sample learning
Learn to learn
Survey
Few-shot learning
Meta learning
AI总结
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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Scheduled sampling for one-shot learning via matching network通过匹配网络进行一次性学习的计划采样.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
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