Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy

Understand

Common explanations for shortcut learning assume that the shortcut improves prediction under the training distribution but not in the test distribution.

• Thus, models trained via the typical gradient-based optimization of cross-entropy, which we call default-ERM, utilize the shortcut.
• However, even when the stable feature determines the label in the training distribution and the shortcut does not provide any additional information, like in perception tasks, default-ERM still exhibits shortcut learning.
• Why are such solutions preferred when the loss for default-ERM can be driven to zero using the stable feature alone? By studying a linear perception task, we show that default-ERM's preference for maximizing the margin leads to models that depend more on the shortcut than the stable feature, even without overparameterization.