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Few-Shot Learning Through an Information Retrieval Lens

arXiv 1707.02610 ↗First posted 9 Jul 2017cs.LGcited by 51

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Abstract (arXiv)

Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the increased importance of maximally leveraging all the available information in this low-data regime. We define a training objective that aims to extract as much information as possible from each training batch by effectively optimizing over all relative orderings of the batch points simultaneously. In particular, we view each batch point as a `query' that ranks the remaining ones based on its predicted relevance to them and we define a model within the framework of structured prediction to optimize mean Average Precision over these rankings. Our method achieves impressive results on the standard few-shot classification benchmarks while is also capable of few-shot retrieval.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jul 2017.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 23; 2022: 16; 2023: 6; 2024: 4; 2025: 0; 2026: 1.

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  1. 2021LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5cited by 103
  2. 2023In-Context Learning with Iterative Demonstration Selectioncited by 74
  3. 2021Deep Learning for Instance Retrieval: A Surveycited by 70
  4. 2018Task-Embedded Control Networks for Few-Shot Imitation Learningcited by 65
  5. 2022Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentationcited by 65
  6. 2023Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learningcited by 55
  7. 2022Retrieval-Enhanced Machine Learningcited by 46
  8. 2021von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learningcited by 42
  9. 2022Continual Training of Language Models for Few-Shot Learningcited by 33
  10. 2021Ranking Distance Calibration for Cross-Domain Few-Shot Learningcited by 27
  11. 2021Binocular Mutual Learning for Improving Few-shot Classificationcited by 26
  12. 2021Few-Shot Learning with Class Imbalancecited by 18
38 more
  1. 2021Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learningcited by 17
  2. 2022Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentationcited by 17
  3. 2021Few-Shot Learning with a Strong Teachercited by 16
  4. 2023Dual Adaptive Representation Alignment for Cross-domain Few-shot Learningcited by 15
  5. 2021Hierarchical Variational Memory for Few-shot Learning Across Domainscited by 14
  6. 2023Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?cited by 11
  7. 2021What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Datacited by 10
  8. 2021A Comparative Review of Recent Few-Shot Object Detection Algorithmscited by 10
  9. 2022Few-shot Metric Learning: Online Adaptation of Embedding for Retrievalcited by 8
  10. 2021On the Importance of Firth Bias Reduction in Few-Shot Classificationcited by 7
  11. 2022What GPT Knows About Who is Whocited by 7
  12. 2022Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learningcited by 7
  13. 2021MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samplescited by 6
  14. 2021Contextualizing Meta-Learning via Learning to Decomposecited by 5
  15. 2023Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learningcited by 5
  16. 2021DAMSL: Domain Agnostic Meta Score-based Learningcited by 4
  17. 2021Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learningcited by 4
  18. 2021Multi-Domain Few-Shot Learning and Dataset for Agricultural Applicationscited by 3
  19. 2021Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learningcited by 3
  20. 2022Similarity Learning based Few Shot Learning for ECG Time Series Classificationcited by 3
  21. 2022Towards Scalable Hyperbolic Neural Networks using Taylor Series Approximationscited by 3
  22. 2022Exploring Effective Knowledge Transfer for Few-shot Object Detectioncited by 3
  23. 2022Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrievalcited by 2
  24. 2021Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Miningcited by 1
  25. 2021Morphable Detector for Object Detection on Demandcited by 1
  26. 2022BaseTransformers: Attention over base data-points for One Shot Learningcited by 1
  27. 2024Harnessing the Power of Local Representations for Few-Shot Classificationcited by 1
  28. 2024Detect an Object At Once without Fine-tuningcited by 1
  29. 2021Connecting Images through Time and Sources: Introducing Low-data, Heterogeneous Instance Retrievalcited by 0
  30. 2021One-shot Learning with Absolute Generalizationcited by 0
  31. 2021Learning by Examples Based on Multi-level Optimizationcited by 0
  32. 2022A Framework of Meta Functional Learning for Regularising Knowledge Transfercited by 0
  33. 2022Learning Primitive-aware Discriminative Representations for Few-shot Learningcited by 0
  34. 2022CobNet: Cross Attention on Object and Background for Few-Shot Segmentationcited by 0
  35. 2022Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptationcited by 0
  36. 2023Enhancing Instance-Level Image Classification with Set-Level Labelscited by 0
  37. 2024LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learningcited by 0
  38. 2024Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learningcited by 0

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