mlsanityData to 7 Oct 2026

Towards Few-Shot Learning in the Open World: A Review and Beyond

arXiv 2408.09722 ↗First posted 19 Aug 2024cs.LG stat.MLcited by 5

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Abstract (arXiv)

Human intelligence is characterized by our ability to absorb and apply knowledge from the world around us, especially in rapidly acquiring new concepts from minimal examples, underpinned by prior knowledge. Few-shot learning (FSL) aims to mimic this capacity by enabling significant generalizations and transferability. However, traditional FSL frameworks often rely on assumptions of clean, complete, and static data, conditions that are seldom met in real-world environments. Such assumptions falter in the inherently uncertain, incomplete, and dynamic contexts of the open world. This paper presents a comprehensive review of recent advancements designed to adapt FSL for use in open-world settings. We categorize existing methods into three distinct types of open-world few-shot learning: those involving varying instances, varying classes, and varying distributions. Each category is discussed in terms of its specific challenges and methods, as well as its strengths and weaknesses. We standardize experimental settings and metric benchmarks across scenarios, and provide a comparative analysis of the performance of various methods. In conclusion, we outline potential future research directions for this evolving field. It is our hope that this review will catalyze further development of effective solutions to these complex challenges, thereby advancing the field of artificial intelligence.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 98 of its 180 references that are in the map. First posted on arXiv 19 Aug 2024.

Builds on

98 of 180 references.

References · 98

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38 more
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50 most cited of 98 All 98 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanationscited by 3
  2. 2025FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learningcited by 1
  3. 2025Hyperbolic Coarse-to-Fine Few-Shot Class-Incremental Learningcited by 0
  4. 2026SRasP: Self-Reorientation Adversarial Style Perturbation for Cross-Domain Few-Shot Learningcited by 0
  5. 2026GAFT: Geo-Anchored Fine-Tuning for Hazard Identification from Rare Failurescited by 0