Resampling techniques like SMOTE (Synthetic Minority Over-sampling Technique) can also be implemented in PyTorch. There are libraries such as `imbalanced-learn` that provide interfaces to integrate these techniques with PyTorch models.

Lastly, using anomaly detection methods like One-Class SVMs or Isolation Forests can also be beneficial for detecting and handling imbalanced classes. PyTorch offers integration with popular anomaly detection libraries like `scikit-learn` or `pyod`.

Ensemble methods, such as bagging or boosting, can also be effective for handling imbalanced datasets. By training multiple models on different subsets of the data or assigning different weights to instances, the overall performance can improve.

Another approach is to use data augmentation techniques, such as oversampling the minority class or undersampling the majority class. The `torchvision.transforms` module offers various transformations that can be applied to the dataset during training to create additional balanced samples.

One common method is to use class weights during training, which assigns higher weights to underrepresented classes to balance their impact on the overall loss function. PyTorch provides the `torch.nn.CrossEntropyLoss` function that accepts a `weight` argument to accommodate this.