Efficient Semi-Supervised Adversarial Training via Latent Clustering-Based Data Reduction
#adversarial training #semi‑supervised learning #latent clustering #data reduction #training efficiency #robust models #memory usage #synthetic data
📌 Key Takeaways
- Semi‑supervised adversarial training (SSAT) leverages large sets of unlabeled or synthetic data, which traditionally leads to high training time and memory costs.
- The new approach applies latent clustering to identify and retain representative data points, thereby reducing the dataset size without compromising model performance.
- Experimental results demonstrate comparable or improved robustness against adversarial attacks while using fewer samples.
- Training time and computational resource consumption are significantly lowered, enabling broader applicability in resource‑constrained settings.
- The methodology enhances scalability of SSAT, paving the way for more efficient deployment of robust models.
📖 Full Retelling
The authors of a recent arXiv submission (arXiv:2501.10466v3) introduce an efficient semi‑supervised adversarial training framework that cuts the amount of training data needed for robust models by employing latent clustering. The proposal is made in the context of adversarial robustness research, announced in January 2025 on arXiv, and aims to reduce both training time and memory usage while maintaining high robustness levels in deep neural networks.
🏷️ Themes
Adversarial robustness, Semi‑supervised learning, Data efficiency, Machine learning optimization
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Original Source
arXiv:2501.10466v3 Announce Type: replace-cross
Abstract: Learning robust models under adversarial settings is widely recognized as requiring a considerably large number of training samples. Recent work proposes semi-supervised adversarial training (SSAT), which utilizes external unlabeled or synthetically generated data and is currently the state of the art. However, SSAT requires substantial extra data to attain high robustness, resulting in prolonged training time and increased memory usage.
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