pseudolabelingtekniikoilla

Pseudolabeling is a semi-supervised learning technique used to improve the performance of machine learning models by leveraging unlabeled data. The core idea is to first train a model on a labeled dataset, and then use this trained model to predict labels for a separate set of unlabeled data. These predicted labels, known as "pseudolabels," are then treated as if they were true labels.

Once the pseudolabels are generated, the original labeled data is combined with the unlabeled data that has now been assigned pseudolabels. This larger, combined dataset is then used to retrain the model. The assumption is that the pseudolabels, while not perfect, are sufficiently accurate to guide the model towards learning more robust representations and improving its generalization capabilities.

Various strategies exist for generating and using pseudolabels. A common approach involves setting a confidence threshold, where only predictions with a probability above this threshold are considered for pseudolabeling. This helps to filter out less reliable predictions. Another technique is to iteratively retrain the model, generating new pseudolabels at each stage and refining the model's performance. Pseudolabeling is particularly useful in scenarios where obtaining large amounts of labeled data is expensive or time-consuming.