A related dataset is Waterbirds, described in Sagawa et al (2020), where you want to classify birds as landbirds or waterbirds regardless of whether they happen to be on a water or land background.
The main difference from HappyFaces is that in Waterbirds the correlation between bird type and background is imperfect, although strong. By contrast, HappyFaces has perfect spurious correlation on the training set. Of course you could filter Waterbirds to make the spurious correlation perfect to get an equally challenging but more natural dataset.
A related dataset is Waterbirds, described in Sagawa et al (2020), where you want to classify birds as landbirds or waterbirds regardless of whether they happen to be on a water or land background.
The main difference from HappyFaces is that in Waterbirds the correlation between bird type and background is imperfect, although strong. By contrast, HappyFaces has perfect spurious correlation on the training set. Of course you could filter Waterbirds to make the spurious correlation perfect to get an equally challenging but more natural dataset.
Very Interesting, thank you.