CNNs & Representation Learning

Convolutional layers exploit spatial structure: local patterns, translation equivariance, parameter sharing.

Practical playbook

1. Start from a pretrained backbone when data is limited
2. Freeze early layers, fine-tune later ones
3. Monitor train/val carefully under augmentation

Beyond images

The same inductive biases inspired 1D convs for sequences — though transformers now dominate many modalities.