Prerequisites & Notation

This chapter requires familiarity with CNNs (Chapter 27), training infrastructure (Chapter 26), and denoising networks (Chapter 27).

CNNs and ResNet (Chapter 27)(Review ch27)
Self-check: Can you build and train a ResNet?
DnCNN/DRUNet denoisers (Chapter 27)(Review ch27)
Self-check: Do you understand residual learning for denoising?
Training infrastructure (Chapter 26)(Review ch26)
Self-check: Can you save/load checkpoints and manage LR schedules?

Symbol	Meaning	Introduced
$f_{\\text{pre}}$	Pre-trained model (backbone)	s01
$\\mathcal{D}_\\text{source}, \\mathcal{D}_\\text{target}$	Source and target domain datasets	s03
$\\text{prox}_{\\sigma D}$	Proximal operator using denoiser $D$	s02