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Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation

arXiv 2311.17096 ↗First posted 28 Nov 2023cs.CVcited by 0

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Abstract (arXiv)

Few-shot learning (FSL) aims to develop a learning model with the ability to generalize to new classes using a few support samples. For transductive FSL tasks, prototype learning and label propagation methods are commonly employed. Prototype methods generally first learn the representative prototypes from the support set and then determine the labels of queries based on the metric between query samples and prototypes. Label propagation methods try to propagate the labels of support samples on the constructed graph encoding the relationships between both support and query samples. This paper aims to integrate these two principles together and develop an efficient and robust transductive FSL approach, termed Prototype-based Soft-label Propagation (PSLP). Specifically, we first estimate the soft-label presentation for each query sample by leveraging prototypes. Then, we conduct soft-label propagation on our learned query-support graph. Both steps are conducted progressively to boost their respective performance. Moreover, to learn effective prototypes for soft-label estimation as well as the desirable query-support graph for soft-label propagation, we design a new joint message passing scheme to learn sample presentation and relational graph jointly. Our PSLP method is parameter-free and can be implemented very efficiently. On four popular datasets, our method achieves competitive results on both balanced and imbalanced settings compared to the state-of-the-art methods. The code will be released upon acceptance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 42 references that are in the map. First posted on arXiv 28 Nov 2023.

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30 of 42 references.

References · 30

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18 more
  1. 2021Free Lunch for Few-shot Learning: Distribution Calibrationcited by 165
  2. 2019Charting the Right Manifold: Manifold Mixup for Few-shot Learningcited by 136
  3. 2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 126
  4. 2019Prototype Rectification for Few-Shot Learningcited by 97
  5. 2019Attributed Graph Clustering via Adaptive Graph Convolutioncited by 96
  6. 2020Laplacian Regularized Few-Shot Learningcited by 92
  7. 2020Embedding Propagation: Smoother Manifold for Few-Shot Classificationcited by 80
  8. 2020Leveraging the Feature Distribution in Transfer-based Few-Shot Learningcited by 74
  9. 2020Instance Credibility Inference for Few-Shot Learningcited by 57
  10. 2020Iterative label cleaning for transductive and semi-supervised few-shot learningcited by 40
  11. 2020TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classificationcited by 30
  12. 2023Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinementcited by 30
  13. 2022Realistic Evaluation of Transductive Few-Shot Learningcited by 27
  14. 2022Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroidcited by 20
  15. 2023Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddingscited by 11
  16. 2022Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classificationcited by 10
  17. 2023Prototypes-oriented Transductive Few-shot Learning with Conditional Transportcited by 8
  18. 2023Adaptive manifold for imbalanced transductive few-shot learningcited by 4

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