Improving the Efficiency of Self-Supervised Adversarial Training through Latent Clustering-Based Selection

• URL: http://arxiv.org/abs/2501.10466v1
• Date: Wed, 15 Jan 2025 15:47:49 GMT
• Title: Improving the Efficiency of Self-Supervised Adversarial Training through Latent Clustering-Based Selection
• Authors: Somrita Ghosh, Yuelin Xu, Xiao Zhang,
• Abstract summary: adversarially robust learning is widely recognized to demand significantly more training examples. Recent works propose the use of self-supervised adversarial training with external or synthetically generated unlabeled data to enhance model robustness. We propose novel methods to strategically select a small subset of unlabeled data essential for SSAT and robustness improvement.
• Score: 2.7554677967598047
• License:
• Abstract: Compared with standard learning, adversarially robust learning is widely recognized to demand significantly more training examples. Recent works propose the use of self-supervised adversarial training (SSAT) with external or synthetically generated unlabeled data to enhance model robustness. However, SSAT requires a substantial amount of extra unlabeled data, significantly increasing memory usage and model training times. To address these challenges, we propose novel methods to strategically select a small subset of unlabeled data essential for SSAT and robustness improvement. Our selection prioritizes data points near the model's decision boundary based on latent clustering-based techniques, efficiently identifying a critical subset of unlabeled data with a higher concentration of boundary-adjacent points. While focusing on near-boundary data, our methods are designed to maintain a balanced ratio between boundary and non-boundary data points to avoid overfitting. Our experiments on image benchmarks show that integrating our selection strategies into self-supervised adversarial training can largely reduce memory and computational requirements while achieving high model robustness. In particular, our latent clustering-based selection method with k-means is the most effective, achieving nearly identical test-time robust accuracies with 5 to 10 times less external or generated unlabeled data when applied to image benchmarks. Additionally, we validate the generalizability of our approach across various application scenarios, including a real-world medical dataset for COVID-19 chest X-ray classification.

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