"Models can learn patterns that correlate with training labels rather than the intended underlying concepts, producing shortcut solutions that fail under unfamiliar conditions."
Geirhos and colleagues identify shortcut learning as a recurring limitation of deep neural networks, showing how models can achieve strong benchmark performance through unintended predictive cues. Their paper directly supports the book's distinction between learning useful statistical patterns and developing representations that generalize reliably beyond the training environment.
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