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Found 2 papers, 1 papers with code
Diagnosing Model Performance Under Distribution Shift
In order to do this, we define a hypothetical distribution on $X$ consisting of values common in both training and target, over which it is easy to compare $Y \mid X$ and thus predictive performance.
Are all negatives created equal in contrastive instance discrimination?
Using methodology from MoCo v2 (Chen et al., 2020), we divided negatives by their difficulty for a given query and studied which difficulty ranges were most important for learning useful representations.
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