An alternative method is to use a combination of under- and oversampling techniques such as SMOTE combined with Tomek links, which can remove noisy samples and generate synthetic examples. Additionally, you can experiment with different network architectures such as focal convolutional neural networks or dual path networks that are specifically designed to handle imbalanced class distributions. Lastly, consider using online hard example mining or online bootstrapping techniques to make the training process more robust to class imbalance.

One effective strategy is to use weighted loss functions such as `torch.nn.CrossEntropyLoss` or `torch.nn.BCEWithLogitsLoss` with appropriate class weights. Another option is to use oversampling or undersampling techniques to balance the class distribution in the training data. Additionally, you can use techniques like data augmentation or ensemble learning to improve the model's ability to handle class imbalance.

In addition to the aforementioned techniques, you can try using focal loss, which gives more emphasis to hard-to-classify examples by down-weighting the well-classified ones. Another approach is to apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples of the minority class. You can also explore advanced techniques like ADASYN (Adaptive Synthetic Sampling). Lastly, consider metric-based approaches like AUC-PRC (Area Under the Precision-Recall Curve) or AUC-ROC (Area Under the Receiver Operating Characteristic Curve) to evaluate model performance in class-imbalanced scenarios.