How do loss surfaces affect generalization in deep learning?

The geometry of loss surfaces influences how well models generalize to unseen data.

Flat minima are associated with better generalization because small perturbations in parameters do not significantly affect performance. Sharp minima may indicate overfitting to training data. The shape of the loss surface is influenced by data distribution, architecture, and optimization method.