Equation 5 · Training on Your Own Output: Synthetic Data and What It Does to a Distribution
What does this equation mean?
Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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Symbol k
k+1 means one index position after k. In the article, use the nearby sentence to see what each position counts.
How to interpret it
Read this expression with the definitions, units, and assumptions supplied by the article.
What the article says around this equation
in which generation k+1 sees n samples drawn from its immediate predecessor and nothing else . The original data are gone. No filter selects among the samples. The lineage is single. Under those conditions, degradation compounds because there is no channel by which an error introduced at generation k can ever be corrected.
Sources cited in the article section
- [2] Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data ↗
- [1] AI models collapse when trained on recursively generated data ↗
- [5] Position: Model Collapse Does Not Mean What You Think ↗
These citations give research context. Read each source to check which claims it supports.
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