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
This approach significantly reduces the amount of data and computational resources needed for the new task.