Achieving high model robustness under adversarial settings is widely recognized as demanding considerable training samples. Recent works propose semi-supervised adversarial training (SSAT) methods with external unlabeled or synthetically generated data, which are the current state-of-the-art. However, SSAT requires substantial extra data to attain high robustness, resulting in prolonged training time and increased memory usage. In this paper, we propose unlabeled data reduction strategies to improve the efficiency of SSAT. Specifically, we design novel latent clustering-based techniques to select or generate a small critical subset of data samples near the model's decision boundary. While focusing on boundary-adjacent points, our methods maintain a balanced ratio between boundary and non-boundary data points to avoid overfitting. Comprehensive experiments on benchmark datasets demonstrate that our methods can significantly reduce SSAT's data requirement and computation costs while preserving its strong robustness advantages. In particular, our latent-space selection scheme based on k-means clustering and our guided DDPM fine-tuning approach with LCG-KM are the most effective, achieving nearly identical robust accuracies with 5x to 10x less unlabeled data and approximately 4x less total runtime.

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