SSL models, or Semi-Supervised Learning models, differ from traditional deep learning models primarily in how they utilize labeled and unlabeled data for training. Traditional deep learning models typically require a large amount of labeled data to achieve high performance. This means that every data point used for training must have an associated label, which can be time-consuming and expensive to obtain. In contrast, SSL models operate on the premise that while labeled data is valuable, there is often an abundance of unlabeled data available. These models leverage both types of data to improve training efficiency and model performance.
One key aspect of SSL models is their ability to learn from unlabeled data by employing different techniques, such as self-training or consistency regularization. For instance, self-training might involve first training a model using the available labeled data, then using this model to predict labels for a portion of the unlabeled data. The most confident predictions are then added to the training set, and the model undergoes further training. This iterative process can lead to better generalization across the entire dataset, as the model learns to capture the underlying structure of the data rather than relying solely on limited labeled examples.
Furthermore, SSL models can reduce the dependency on labeled data, which can be particularly beneficial in areas where data labeling is difficult. For example, in medical imaging, obtaining accurate labels often requires specialist knowledge. By employing SSL techniques, a model can make use of the vast amount of available imaging data without exhaustive labeling, thereby improving performance while minimizing the labeling burden. This flexibility makes SSL models a practical choice in many real-world applications, allowing developers to build effective models even with limited labeled data.