mlsanityData to 14 Oct 2026

Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation

arXiv 2203.03810 ↗First posted 8 Mar 2022cs.LG cs.AI cs.CRcited by 4

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Abstract (arXiv)

The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme to improve robustness for models towards unforeseen malicious inputs in the black-box test settings. Specifically, we introduce a noised-based data augmentation method which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD adversarial perturbations. The proposed method is built on lightweight algorithms and proved highly effective based on comprehensive evaluations, showing good efficiency on computation cost and robustness enhancement. In addition, we share our insights about the data-centric robust machine learning gained from our experiments.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

14 of 21 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
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  10. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,517
  11. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 678
  12. 2020Overfitting in adversarially robust deep learningcited by 506
2 more
  1. 2020Bag of Tricks for Adversarial Trainingcited by 200
  2. 2022Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfercited by 78

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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferabilitycited by 32
  2. 2023Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)cited by 4
  3. 2023Self-Improving-Leaderboard(SIL): A Call for Real-World Centric Natural Language Processing Leaderboardscited by 1
  4. 2024Harnessing the Power of Noise: A Survey of Techniques and Applicationscited by 0