Unverified50% confidenceFactExact time
Adversarial examples are specially crafted test inputs that cause a model to produce incorrect outputs by applying imperceptibly small perturbations to input data.
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50%
Confidence
Long-term
Relevance
9/19/2026
First Seen
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UnverifiedThe core principle of adversarial example attacks is to apply imperceptibly small perturbations to input data that are nearly undetectable to the human eye, causing a trained deep learning model to produce completely incorrect outputs.80% similarUnverifiedGoodfellow et al. discovered the adversarial example phenomenon in 2014, whereby imperceptibly small perturbations to the input, invisible to the human eye, can cause a model to produce incorrect outputs.79% similarVerified若用含测试集的全量数据计算均值和标准差，会导致预处理泄漏，使评估指标虚高，无法真实反映模型泛化能力77% similarUnverifiedThe study proposes using a causal counterfactual criterion to suppress false starts: if muting the user input leaves the model's output distribution nearly unchanged, the event is identified as a false start and suppressed.76% similarUnverifiedWhen troubleshooting a model that fails to converge, one can first test whether the model can overfit a very small data subset; if it cannot learn even a few samples, there is most likely a bug in the code or configuration.73% similar
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