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Feature augmentation based on information fusion rectification for few-shot image classification.

Hang Wang1, Shengzhao Tian1, Yan Fu1,2

  • 1Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, China.

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|March 3, 2023
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Summary
This summary is machine-generated.

A new information fusion rectification (IFR) algorithm improves few-shot image classification by rectifying support set distributions. This method enhances data augmentation, leading to significant accuracy gains on challenging few-shot tasks.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Few-shot image classification faces overfitting due to insufficient data.
  • Current non-parametric data augmentation methods may introduce deviations by not fully accounting for data distribution differences.

Purpose of the Study:

  • To propose a novel algorithm, Information Fusion Rectification (IFR), to address limitations in few-shot image classification data augmentation.
  • To improve the accuracy of few-shot image classification by rectifying the distribution of support set data.

Main Methods:

  • The IFR algorithm leverages relationships within data, including base and new classes, and support/query sets.
  • It rectifies the support set distribution in new classes before data augmentation.
  • Feature expansion is achieved by sampling from the rectified normal distribution.

Main Results:

  • The IFR algorithm demonstrated improved accuracy compared to existing image augmentation methods.
  • Accuracy gains were observed between 1.84-4.66% on 5-way 1-shot tasks.
  • Accuracy gains ranged from 0.99-1.43% on 5-way 5-shot tasks across three datasets.

Conclusions:

  • The proposed IFR algorithm effectively rectifies support set distributions for enhanced data augmentation.
  • IFR offers a promising approach to mitigate overfitting and boost performance in few-shot image classification.