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Related Experiment Video

Updated: Nov 8, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Implicit adversarial data augmentation and robustness with Noise-based Learning.

Priyadarshini Panda1, Kaushik Roy2

  • 1Department of Electrical Engineering, New Haven, Yale University, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|April 24, 2021
PubMed
Summary
This summary is machine-generated.

We developed Noise-based Learning (NoL) to train neural networks robust to adversarial attacks. This method uses learned noise for data augmentation, enhancing generalization and significantly improving adversarial defense capabilities.

Keywords:
Adversarial robustnessDeep learningPrincipal Component Analysis

Related Experiment Videos

Last Updated: Nov 8, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

820

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Neural networks are vulnerable to adversarial attacks.
  • Existing defense mechanisms have limitations.

Purpose of the Study:

  • Introduce a novel Noise-based Learning (NoL) approach.
  • Enhance intrinsic robustness of neural networks against adversarial attacks.

Main Methods:

  • Incorporating random noise learning into the training process.
  • Utilizing the same loss function for posterior maximization.
  • Applying Principal Component Analysis for adversarial data visualization.

Main Results:

  • Learned noise acts as implicit adversarial data augmentation.
  • Improved adversarial generalization capability.
  • Demonstrated strong performance against diverse attacks on benchmarks like MNIST, CIFAR10, CIFAR100, and Tiny ImageNet.

Conclusions:

  • NoL offers intrinsic adversarial robustness.
  • Combining NoL with adversarial training yields superior defense against white-box and black-box attacks.