Unverified50% confidenceCautionExact time
合成数据可能出现模式坍塌导致多样性不足、遗漏尾部分布中稀有模式、在高维数据中引入不真实相关性等质量问题
1
Sources
50%
Confidence
Long-term
Relevance
8/12/2026
First Seen
Sources
Related Claims
UnverifiedIf only the data of newly added classes is used to fine-tune an expanded classification head, the model is highly prone to catastrophic forgetting, rapidly losing its ability to recognize the original classes.71% similarUnverified错误的归纳偏置会让模型花费容量拟合数据中不存在的虚假模式，导致分布外数据上严重失效71% similarUnverified数据多样性对模型泛化能力的影响往往比数据规模更为根本，仅在特定分布上过度拟合的模型在真实部署时表现骤降69% similar
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