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It doesn't need to create wrong answers. It's enough to recall people who gave wrong answers.
by ruszki 2y ago
It doesn't need to create wrong answers. It's enough to recall people who gave wrong answers.
- ben_w 2y agoI've heard the term originated in image recognition, where models would "see" things that weren't there. You can still get that with zero bad labels in a supervised training set. Multiple causes for the same behaviour makes progress easier, but knowing if it's fully solved harder.