Transfer Learning

Transfer learning reuses a model trained on one task or dataset as the starting point for a related task, so that a new model can be trained with far less data and compute than training from scratch.

A model pre-trained on a large dataset, such as an image classifier trained on millions of general images or a language model trained on large text corpora, has already learned general features. For a new task, its early layers can be kept frozen as a feature extractor while new output layers are trained, or the whole model can be fine-tuned with a low learning rate on the target data.

Transfer learning is standard practice in industrial computer vision, where defect datasets are small: networks pre-trained on general images are adapted to recognise scratches, stains or missing components. In language applications, pre-trained models are adapted to domain vocabulary such as maintenance logs or technical documentation.

Transfer works best when source and target data share features; very different domains, such as thermal or X-ray images, may benefit less. Pre-trained models carry the biases and licence terms associated with their training, which should be checked. Validation must still use representative data from the target process.

Key points

Starts from a model pre-trained on a large, general dataset
Reduces the data and compute needed for a new task
Standard practice for industrial vision with small defect datasets
Licence terms and biases of the pre-trained model carry over

Where AiVibe comes in

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Related terms

Terms that refer to Transfer Learning

Image ClassificationAI & Machine Learning