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Related Experiment Video

Updated: Apr 8, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Individual Active Learning: Safeguarding against Out-of-Distribution Challenges in Neural Networks.

Shachar Shayovitz1, Koby Bibas1, Meir Feder1

  • 1School of Electrical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.

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|February 23, 2024
PubMed
Summary

This study introduces an efficient active learning (AL) algorithm that minimizes data annotation needs, especially for out-of-distribution data. The novel approach significantly reduces training set sizes across multiple datasets.

Keywords:
active learningdeep active learningindividual sequencesnormalized maximum likelihoodout-of-distributionuniversal prediction

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Area of Science:

  • Machine Learning
  • Computer Science

Background:

  • Active learning (AL) aims to reduce data annotation costs by strategically selecting training samples.
  • Traditional AL methods often assume data distribution consistency, which is not always feasible, particularly in privacy-sensitive scenarios.

Purpose of the Study:

  • To develop an efficient active learning algorithm for individual settings, focusing on minimizing min-max regret.
  • To address the computational complexity of active learning criteria for neural networks.

Main Methods:

  • The study proposes an active learning criterion based on minimizing min-max regret on a small unlabeled test set sample.
  • An efficient algorithm is developed to approximate this criterion for neural networks, tackling computational challenges.

Main Results:

  • The proposed algorithm significantly reduces the required training set size, with reductions of up to 15.4% on CIFAR10, 11% on EMNIST, and 35.1% on MNIST.
  • The effectiveness is particularly pronounced when dealing with out-of-distribution data.

Conclusions:

  • The developed active learning algorithm offers an efficient solution for data selection, especially in scenarios with distribution shifts.
  • This approach enhances model performance while minimizing the need for extensive data annotation, proving valuable for privacy-sensitive applications.