mlsanityData to 7 Oct 2026

Local Propagation for Few-Shot Learning

arXiv 2101.01480 ↗First posted 5 Jan 2021cs.CVcited by 4

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Abstract (arXiv)

The challenge in few-shot learning is that available data is not enough to capture the underlying distribution. To mitigate this, two emerging directions are (a) using local image representations, essentially multiplying the amount of data by a constant factor, and (b) using more unlabeled data, for instance by transductive inference, jointly on a number of queries. In this work, we bring these two ideas together, introducing local propagation. We treat local image features as independent examples, we build a graph on them and we use it to propagate both the features themselves and the labels, known and unknown. Interestingly, since there is a number of features per image, even a single query gives rise to transductive inference. As a result, we provide a universally safe choice for few-shot inference under both non-transductive and transductive settings, improving accuracy over corresponding methods. This is in contrast to existing solutions, where one needs to choose the method depending on the quantity of available data.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 53 of its 73 references that are in the map. First posted on arXiv 5 Jan 2021.

Builds on

53 of 73 references.

References · 53

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38 more
  1. 2019Few-Shot Unsupervised Image-to-Image Translationcited by 222
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  3. 2018Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learningcited by 208
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  38. 2020Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classificationcited by 7

50 most cited of 53 All 53 (JSON)

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Cited by year

Citing papers per year: 2021: 3; 2022: 0; 2023: 1.

4 papers.

  1. 2021Relational Embedding for Few-Shot Classificationcited by 74
  2. 2021Learning to Affiliate: Mutual Centralized Learning for Few-shot Classificationcited by 18
  3. 2023Adaptive manifold for imbalanced transductive few-shot learningcited by 4
  4. 2021ShufaNet: Classification method for calligraphers who have reached the professional levelcited by 0