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  • title: Improving Adversarial Robustness by Sample-Efficient Synthetic Data Generation
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            Improving Adversarial Robustness by Sample-Efficient Synthetic Data Generation
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            Improving Adversarial Robustness by Sample-Efficient Synthetic Data Generation

            Jul 24, 2023

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            Synthetic data generation has become an emerging tool to help improve the adversarial robustness in classification tasks, since robust learning requires a significantly larger amount of training samples compared with standard classification. Among various deep generative models, the diffusion model has been shown to produce high-quality synthetic images and has achieved good performance in improving the adversarial robustness. However, diffusion-type methods are generally slower in data generati…

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            I2
            I2

            ICML 2023

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