transferinlärning

Transfer learning, or transferinlärning in Swedish, is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second, related task. Instead of training a new model from scratch, which can be time-consuming and require vast amounts of data, transfer learning leverages the knowledge gained from a pre-trained model. This pre-trained model has typically been trained on a large dataset for a general purpose, such as image recognition on a massive dataset like ImageNet.

The core idea is that the features learned by the model on the initial task are often general enough to be useful for other tasks. For example, a model trained to classify common objects might have learned to detect edges, textures, and basic shapes. These learned features can then be fine-tuned or adapted for a new, more specific task, like identifying different types of medical images or classifying specific breeds of dogs.

This approach significantly reduces the amount of data and computational resources needed for the new task. It's particularly beneficial in domains where obtaining large labeled datasets is difficult or expensive. The process usually involves taking a pre-trained network, often removing its final output layer, and then adding new layers that are trained on the specific dataset for the new task. Sometimes, earlier layers of the pre-trained network are "frozen" to preserve their learned general features, while later layers are trained more extensively to specialize.