Verified65% confidenceFactExact time
The 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."
3
Sources
65%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
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81% 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.79% 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.77% similarUnverifiedThe understanding of text-to-image models is essentially large-scale statistical association, and generation quality drops significantly when dealing with long-tail concepts that appear rarely in the training set.76% similar
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