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Explore the Power of Dropout on Few-shot Learning

arXiv 2301.11015 ↗First posted 26 Jan 2023cs.CVcited by 0

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Abstract (arXiv)

The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this paper, we explore the power of dropout on few-shot learning and provide some insights about how to use it. Extensive experiments on the few-shot object detection and few-shot image classification datasets, i.e., Pascal VOC, MS COCO, CUB, and mini-ImageNet, validate the effectiveness of our method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 22 references that are in the map. First posted on arXiv 26 Jan 2023.

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References · 19

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  6. 2016Matching Networks for One Shot Learningcited by 2,255
  7. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  8. 2019A Closer Look at Few-shot Classificationcited by 684
  9. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  10. 2020Frustratingly Simple Few-Shot Object Detectioncited by 282
  11. 2018Few-shot Object Detection via Feature Reweightingcited by 265
  12. 2018DropBlock: A regularization method for convolutional networkscited by 262
7 more
  1. 2019Meta R-CNN : Towards General Solver for Instance-level Few-shot Learningcited by 182
  2. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
  3. 2021Free Lunch for Few-shot Learning: Distribution Calibrationcited by 165
  4. 2020Multi-Scale Positive Sample Refinement for Few-Shot Object Detectioncited by 116
  5. 2021Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detectioncited by 80
  6. 2021Hallucination Improves Few-Shot Object Detectioncited by 50
  7. 2018Few-Shot Learning with Metric-Agnostic Conditional Embeddingscited by 40

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