Fig. 3: More training data and dual batch norms are essential to accurate adversarial training. | Nature Communications

Fig. 3: More training data and dual batch norms are essential to accurate adversarial training.

From: Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization

Fig. 3: More training data and dual batch norms are essential to accurate adversarial training.

The classification performance of a standard, an adversarial (blue column), and an adversarially augmented model (red column)) with respect to different amounts of training data. In accordance with our hypotheses, the performance of adversarially trained models were boosted both by employing the dual batch norm and by enlarging the training set. In the case of pneumonia classification, the performance of adversarially trained models was limited and less stable due to an insufficient amount of pneumonia positive cases in the dataset. Data are presented as mean values +/− SD (standard deviation). Note, n = 10,000 redraws are calculated in the bootstrapping analysis to get the mean and SD.

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