"Even when a detector uses additional training data, it can retain blind spots associated with the unfamiliar cases used to develop and evaluate it."
Wang and colleagues show that Outlier Exposure methods can perform extremely well on standard benchmarks while struggling under more challenging large-scale evaluations. Their results demonstrate that detection performance depends partly on the relationship between auxiliary examples and the unfamiliar distributions subsequently encountered.
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