"When image classifiers encounter objects in unfamiliar contexts, their learned shortcuts can cause unexpected recognition failures."
Wen and colleagues investigate deep neural networks that learn misleading shortcuts and experience severe performance degradation when data distributions change. Their experiments across image classification and control tasks support the manuscript's warning that recognition systems can fail when familiar objects appear against backgrounds or in circumstances that differ from training.
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