Diffusion models tackle label imbalance in conditional settings by utilizing techniques that ensure each class is sufficiently represented during the training process. Label imbalance occurs when certain classes in the data have significantly more examples than others, which can lead to biased model predictions. To counter this, diffusion models often employ re-weighting strategies and resampling methods. For instance, when preparing the dataset, developers might assign higher weights to instances of underrepresented classes. This approach enables the model to learn more effectively from minority classes, thereby reducing the risk of overfitting to majority classes during the learning process.
Another common method is to use data augmentation techniques aimed specifically at minority classes. By artificially increasing the number of samples for these classes through transformations, perturbations, or other techniques, developers can help balance the representation in the training set. For example, if a diffusion model is trained to generate images based on class labels, augmenting the images of a minority class with rotations, flips, or color adjustments can help the model learn more robust features associated with that class. This way, the learning dynamics become less skewed, and the model can generate outputs that incorporate characteristics from all classes more evenly.
Additionally, some diffusion models implement tailored sampling strategies where they encourage the selection of minority class examples for generation during specific training iterations or epochs. This targeted focus ensures that over the course of training, the model explores and understands the nuances of less frequent classes. By combining these various techniques—re-weighting, data augmentation, and adaptive sampling—developers can create more balanced and fair outcomes from diffusion models, improving their performance in tasks with label imbalance in conditional settings.