SSL (Semi-Supervised Learning) models manage class imbalance during training using various strategies that help ensure both minority and majority classes are adequately represented. Class imbalance occurs when one class has significantly more examples than another, leading to models that perform poorly on the underrepresented class. SSL leverages both labeled and unlabeled data to mitigate this issue, allowing models to learn from a broader dataset without solely relying on the limited labeled examples.
One common approach is to use data augmentation techniques for the minority class. By applying transformations such as rotation, flipping, or scaling to existing minority class samples, the model gains more training examples, which can help balance the dataset. For instance, if a model is trained to classify images of cats and dogs, and there are fewer images of cats, augmenting those images can provide the model more diverse representations of cats, thus enhancing its ability to learn the characteristics of that class. Additionally, using synthetic data generation techniques like SMOTE (Synthetic Minority Over-sampling Technique) can further help increase the representation of the minority class in the dataset.
Another effective method is modifying the loss function to penalize misclassifying the minority class more than the majority class. This can be achieved through techniques like weighted loss functions, which assign higher weights to minority classes. For example, if a binary classification model has 90% positive examples and 10% negative examples, the loss function can be adjusted so that errors on negative examples contribute more to the overall loss. By focusing more on the minority class during training, the model learns to recognize and classify this class more effectively, which is essential for real-world applications where the cost of misclassifying an underrepresented class can be significant.