Pseudo-Non-Linear Data Augmentation via Energy Minimization

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Brief Summary

We propose a novel and interpretable data augmentation method based on energy-based modeling and principles from information geometry. Unlike black-box generative models, which rely on deep neural networks, our approach replaces these non-interpretable transformations with explicit, theoretically grounded ones, ensuring interpretability and strong guarantees such as energy minimization. Central to our method is the introduction of the backward projection* algorithm, which reverses dimension reduction to generate new data. Empirical results demonstrate that our method achieves competitive performance with black-box generative models while offering greater transparency and interpretability.

Citation

@article{hu2024pseudo,
  title   = {Pseudo-Non-Linear Data Augmentation via Energy Minimization},
  author  = {Hu, Pingbang and Sugiyama, Mahito},
  journal = {arXiv preprint arXiv:2410.00718},
  year    = {2024}
}