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SENet: A Spectral Filtering Approach to Represent Exemplars for Few-shot Learning

arXiv 2305.18970 ↗First posted 30 May 2023cs.CVcited by 0

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Abstract (arXiv)

Prototype is widely used to represent internal structure of category for few-shot learning, which was proposed as a simple inductive bias to address the issue of overfitting. However, since prototype representation is normally averaged from individual samples, it can appropriately to represent some classes but with underfitting to represent some others that can be batter represented by exemplars. To address this problem, in this work, we propose Shrinkage Exemplar Networks (SENet) for few-shot classification. In SENet, categories are represented by the embedding of samples that shrink towards their mean via spectral filtering. Furthermore, a shrinkage exemplar loss is proposed to replace the widely used cross entropy loss for capturing the information of individual shrinkage samples. Several experiments were conducted on miniImageNet, tiered-ImageNet and CIFAR-FS datasets. The experimental results demonstrate the effectiveness of our proposed method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 30 May 2023.

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24 of 41 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  4. 2020Supervised Contrastive Learningcited by 2,829
  5. 2016Matching Networks for One Shot Learningcited by 2,255
  6. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  7. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 997
  8. 2019A Closer Look at Few-shot Classificationcited by 684
  9. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  10. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  11. 2018TADAM: Task dependent adaptive metric for improved few-shot learningcited by 408
  12. 2018Meta-learning with differentiable closed-form solverscited by 397
12 more
  1. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  2. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 364
  3. 2019Finding Task-Relevant Features for Few-Shot Learning by Category Traversalcited by 101
  4. 2019Infinite Mixture Prototypes for Few-Shot Learningcited by 99
  5. 2019Prototype Rectification for Few-Shot Learningcited by 97
  6. 2019Adaptive Cross-Modal Few-Shot Learningcited by 95
  7. 2020Multimodal Prototypical Networks for Few-shot Learningcited by 48
  8. 2017Gaussian Prototypical Networks for Few-Shot Learning on Omniglotcited by 20
  9. 2019Variational Metric Scaling for Metric-Based Meta-Learningcited by 17
  10. 2014Kernel Mean Shrinkage Estimatorscited by 16
  11. 2013Kernel Mean Estimation and Stein's Effectcited by 11
  12. 2022Kernel Relative-prototype Spectral Filtering for Few-shot Learningcited by 4

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