Transfer Learning
Transfer learning reuses knowledge learned from one task or domain to help with another. A pretrained model can supply fixed features or provide weights that are adapted to the target data.
A factory could start with an image model pretrained on a broad collection of objects and replace its final classifier with a head for “defective” and “acceptable.” The model’s feature extractor supplies a useful starting representation for the factory’s labeled photographs.
In one approach, freeze the extractor and train only the new head. In another, also update some pretrained layers: this is fine-tuning. Both are transfer learning. Freezing means the extractor’s weights stay fixed during the head’s training; it does not mean the whole pipeline has no trainable parameters.
Reuse can reduce the training work and labeled data needed, but the benefit depends on how well the source representation fits the target. Compare held-out results and avoid assuming general object features capture every manufacturing defect. Transfer learning describes reuse across a task or domain boundary, not a guarantee of better accuracy.
Sources
- PyTorch: Transfer Learning for Computer Vision — Shows fine-tuning a pretrained network and freezing it as a fixed feature extractor.
Go deeper
- Keras: Transfer learning and fine-tuning docs
Work through layer freezing, a new classifier and careful unfreezing for adaptation.