mlsanityData to 14 Oct 2026

What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data

arXiv 2106.06410 ↗First posted 11 Jun 2021cs.LG cs.AIcited by 10

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Abstract (arXiv)

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include: heavy reliance on massive training data, limited generalizability and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 167 of its 346 references that are in the map. First posted on arXiv 11 Jun 2021.

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167 of 346 references.

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Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 4.

10 papers.

  1. 2022Ontology-enhanced Prompt-tuning for Few-shot Learningcited by 24
  2. 2021Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Surveycited by 23
  3. 2022Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overviewcited by 16
  4. 2021Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learningcited by 9
  5. 2024Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignmentcited by 8
  6. 2024Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Predictioncited by 4
  7. 2024Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performancecited by 4
  8. 2024Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRTcited by 3
  9. 2023A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematicscited by 1
  10. 2022Reinforcement Learning in Education: A Multi-Armed Bandit Approachcited by 0