mlsanityData to 14 Oct 2026

Anchor Data Augmentation

arXiv 2311.06965 ↗First posted 12 Nov 2023cs.LG stat.MLcited by 6

Not on the map. Why?

Abstract (arXiv)

We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation, which is in contrast to the current state-of-the-art domain-agnostic solutions that rely on the Mixup literature. Our Anchor Data Augmentation (ADA) uses several replicas of the modified samples in AR to provide more training examples, leading to more robust regression predictions. We apply ADA to linear and nonlinear regression problems using neural networks. ADA is competitive with state-of-the-art C-Mixup solutions.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 36 of its 54 references that are in the map. First posted on arXiv 12 Nov 2023.

Builds on

36 of 54 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  6. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  7. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  8. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  9. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,375
  10. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  11. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 536
  12. 2017Data Augmentation Generative Adversarial Networkscited by 278
24 more
  1. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 264
  2. 2020Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixupcited by 261
  3. 2019Fast AutoAugmentcited by 243
  4. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 227
  5. 2020How Does Mixup Help With Robustness and Generalization?cited by 193
  6. 2018DeepDTA: Deep Drug-Target Binding Affinity Predictioncited by 173
  7. 2019Population Based Augmentation: Efficient Learning of Augmentation Policy Schedulescited by 154
  8. 2021Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversitycited by 128
  9. 2018Invariance, Causality and Robustnesscited by 123
  10. 2018Anchor regression: heterogeneous data meets causalitycited by 118
  11. 2019Adversarial AutoAugmentcited by 111
  12. 2021AugMax: Adversarial Composition of Random Augmentations for Robust Trainingcited by 90
  13. 2020On Mixup Regularizationcited by 83
  14. 2021AutoMix: Unveiling the Power of Mixup for Stronger Classifierscited by 69
  15. 2022C-Mixup: Improving Generalization in Regressioncited by 57
  16. 2019Online Hyper-parameter Learning for Auto-Augmentation Strategycited by 38
  17. 2020UniformAugment: A Search-free Probabilistic Data Augmentation Approachcited by 38
  18. 2020OnlineAugment: Online Data Augmentation with Less Domain Knowledgecited by 28
  19. 2015Dropout as data augmentationcited by 27
  20. 2021Data Augmentation with Variational Autoencoders and Manifold Samplingcited by 16
  21. 2021DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximizationcited by 15
  22. 2022Preventing Manifold Intrusion with Locality: Local Mixupcited by 11
  23. 2021RegMix: Data Mixing Augmentation for Regressioncited by 7
  24. 2022Universal Adaptive Data Augmentationcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2024A Survey on Mixup Augmentations and Beyondcited by 20
  2. 2024radarODE: An ODE-Embedded Deep Learning Model for Contactless ECG Reconstruction from Millimeter-Wave Radarcited by 7
  3. 2024ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecastingcited by 3
  4. 2025Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac Feature Monitoring from Radar Signal Componentscited by 2
  5. 2025Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistencycited by 0
  6. 2026Image Synthesis as an Intermediate for Controllable Time Series Generationcited by 0