A deep neural network's performance degrades due to adversarial noise injected during training. Which consequence dominates if the network lacks explicit denoising layers?

💡 Explanation

If a neural network lacks denoising layers, adversarial noise directly corrupts learned features, because the network cannot distinguish signal from noise. Therefore, robustness to perturbations will sharply decrease, rather than overfitting, which is a separate concern, or gradients vanishing, which can be independent.

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