sampling bias impact on data diversity (IMAGE)
Caption
The incentive for cherry picking ⎯ the tendency of users to favor data quality over diversity ⎯ is that data quality is preserved over a greater number of model iterations, but this comes at the expense of an even steeper decline in diversity. Pictured are sample image outputs from a first, third and fifth generation model of fully synthetic loop with sampling bias parameter. With each iteration, the dataset becomes increasingly homogeneous.
Credit
(Image courtesy of Digital Signal Processing Group/Rice University)
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Must credit Digital Signal Processing Group/Rice University.
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Original content