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Aggregated f-average Neural Network applied to Few-Shot Class Incremental Learning

arXiv 2310.05566 ↗First posted 9 Oct 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Ensemble learning leverages multiple models (i.e., weak learners) on a common machine learning task to enhance prediction performance. Basic ensembling approaches average the weak learners outputs, while more sophisticated ones stack a machine learning model in between the weak learners outputs and the final prediction. This work fuses both aforementioned frameworks. We introduce an aggregated f-average (AFA) shallow neural network which models and combines different types of averages to perform an optimal aggregation of the weak learners predictions. We emphasise its interpretable architecture and simple training strategy, and illustrate its good performance on the problem of few-shot class incremental learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 9 Oct 2023.

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24 of 37 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2017Graph Attention Networkscited by 8,923
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  5. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  6. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  7. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  8. 2016Matching Networks for One Shot Learningcited by 2,255
  9. 2013Fine-Grained Visual Classification of Aircraftcited by 1,726
  10. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  11. 2019A Closer Look at Few-shot Classificationcited by 684
  12. 2018End-to-End Incremental Learningcited by 597
12 more
  1. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  2. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  3. 2020Few-Shot Class-Incremental Learningcited by 251
  4. 2019Cross Attention Network for Few-shot Classificationcited by 219
  5. 2019SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learningcited by 195
  6. 2021Few-Shot Incremental Learning with Continually Evolved Classifierscited by 187
  7. 2022Forward Compatible Few-Shot Class-Incremental Learningcited by 127
  8. 2019Edge-labeling Graph Neural Network for Few-shot Learningcited by 114
  9. 2021Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learningcited by 96
  10. 2022Constrained Few-shot Class-incremental Learningcited by 94
  11. 2021Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgettingcited by 33
  12. 2022Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inferencecited by 9

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