Unverified50% confidenceFactExact time
An out-of-distribution scenario refers to a situation where the input data encountered during model inference differs significantly in distribution from the training or historical experience data.
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9/29/2026
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Unverified当生成模型反复在自身生成的数据上训练时,输出分布会逐渐退化,低概率事件被系统性遗忘75% similarUnverifiedThe training data of detection models perpetually lags behind the output distribution of the latest generation technology, a problem referred to in academia as "distribution shift."74% similarVerifiedDistribution shift refers to the statistical distribution difference between the training environment and the deployment environment, and is the direct mechanism by which spurious correlations cause performance collapse.73% similarVerifiedData Drift refers to the continuous degradation of model performance without any errors when the statistical properties of real online data deviate from the training set.70% similarUnverified分布偏移意味着训练集与测试集之间的KL散度不为零,即两个分布之间存在可度量的信息差距70% similar
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